| Rfc | 4981 |
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| Title | Survey of Research towards Robust Peer-to-Peer Networks: Search
Methods |
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| Author | J. Risson, T. Moors |
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| Date | September 2007 |
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| Format: | TXT, HTML |
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| Status: | INFORMATIONAL |
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|
Network Working Group J. Risson
Request for Comments: 4981 T. Moors
Category: Informational University of New South Wales
September 2007
Survey of Research towards Robust Peer-to-Peer Networks:
Search Methods
Status of This Memo
This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.
IESG Note
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IETF disclaims any knowledge of the fitness of this RFC for any
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Abstract
The pace of research on peer-to-peer (P2P) networking in the last
five years warrants a critical survey. P2P has the makings of a
disruptive technology -- it can aggregate enormous storage and
processing resources while minimizing entry and scaling costs.
Failures are common amongst massive numbers of distributed peers,
though the impact of individual failures may be less than in
conventional architectures. Thus, the key to realizing P2P's
potential in applications other than casual file sharing is
robustness.
P2P search methods are first couched within an overall P2P taxonomy.
P2P indexes for simple key lookup are assessed, including those based
on Plaxton trees, rings, tori, butterflies, de Bruijn graphs, and
skip graphs. Similarly, P2P indexes for keyword lookup, information
retrieval and data management are explored. Finally, early efforts
to optimize range, multi-attribute, join, and aggregation queries
over P2P indexes are reviewed. Insofar as they are available in the
primary literature, robustness mechanisms and metrics are highlighted
throughout. However, the low-level mechanisms that most affect
robustness are not well isolated in the literature. Recommendations
are given for future research.
Table of Contents
1. Introduction ....................................................3
1.1. Related Disciplines ........................................6
1.2. Structured and Unstructured Routing ........................7
1.3. Indexes and Queries ........................................9
2. Index Types ....................................................10
2.1. Local Index (Gnutella) ....................................10
2.2. Central Index (Napster) ...................................12
2.3. Distributed Index (Freenet) ...............................13
3. Semantic Free Index ............................................15
3.1. Origins ...................................................15
3.1.1. Plaxton, Rajaraman, and Richa (PRR) ................15
3.1.2. Consistent Hashing .................................16
3.1.3. Scalable Distributed Data Structures (LH*) .........16
3.2. Dependability .............................................17
3.2.1. Static Dependability ...............................17
3.2.2. Dynamic Dependability ..............................18
3.2.3. Ephemeral or Stable Nodes -- O(log n) or
O(1) Hops ..........................................19
3.2.4. Simulation and Proof ...............................20
3.3. Latency ...................................................21
3.3.1. Hop Count and the O(1)-Hop DHTs ....................21
3.3.2. Proximity and the O(log n)-Hop DHTs ................22
3.4. Multicasting ..............................................23
3.4.1. Multicasting vs. Broadcasting ......................23
3.4.2. Motivation for DHT-based Multicasting ..............23
3.4.3. Design Issues ......................................24
3.5. Routing Geometries ........................................25
3.5.1. Plaxton Trees (Pastry, Tapestry) ...................25
3.5.2. Rings (Chord, DKS) .................................27
3.5.3. Tori (CAN) .........................................28
3.5.4. Butterflies (Viceroy) ..............................29
3.5.5. de Bruijn (D2B, Koorde, Distance Halving, ODRI) ....30
3.5.6. Skip Graphs ........................................32
4. Semantic Index .................................................33
4.1. Keyword Lookup ............................................34
4.1.1. Gnutella Enhancements ..............................36
4.1.2. Partition-by-Document, Partition-by-Keyword ........38
4.1.3. Partial Search, Exhaustive Search ..................39
4.2. Information Retrieval .....................................39
4.2.1. Vector Model (PlanetP, FASD, eSearch) ..............41
4.2.2. Latent Semantic Indexing (pSearch) .................43
4.2.3. Small Worlds .......................................43
5. Queries ........................................................44
5.1. Range Queries .............................................45
5.2. Multi-Attribute Queries ...................................48
5.3. Join Queries ..............................................50
5.4. Aggregation Queries .......................................50
6. Security Considerations ........................................52
7. Conclusions ....................................................52
8. Acknowledgments ................................................53
9. References .....................................................54
9.1. Informative References ....................................54
1. Introduction
Peer-to-peer (P2P) networks are those that exhibit three
characteristics: self-organization, symmetric communication, and
distributed control [1]. A self-organizing P2P network
"automatically adapts to the arrival, departure and failure of nodes"
[2]. Communication is symmetric in that peers act as both clients
and servers. It has no centralized directory or control point.
USENET servers and BGP peers have these traits [3] but the emphasis
here is on the flurry of research since 2000. Leading examples
include Gnutella [4], Freenet [5], Pastry [2], Tapestry [6], Chord
[7], the Content Addressable Network (CAN) [8], pSearch [9], and
Edutella [10]. Some have suggested that peers are inherently
unreliable [11]. Others have assumed well-connected, stable peers
[12].
This critical survey of P2P academic literature is warranted, given
the intensity of recent research. At the time of writing, one
research database lists over 5,800 P2P publications [13]. One vendor
surveyed P2P products and deployments [14]. There is also a tutorial
survey of leading P2P systems [15]. DePaoli and Mariani recently
reviewed the dependability of some early P2P systems at a high level
[16]. The need for a critical survey was flagged in the peer-to-peer
research group of the Internet Research Task Force (IRTF) [17].
P2P is potentially a disruptive technology with numerous
applications, but this potential will not be realized unless it is
demonstrated to be robust. A massively distributed search technique
may yield numerous practical benefits for applications [18]. A P2P
system has potential to be more dependable than architectures relying
on a small number of centralized servers. It has potential to evolve
better from small configurations -- the capital outlays for high
performance servers can be reduced and spread over time if a P2P
assembly of general purpose nodes is used. A similar argument
motivated the deployment of distributed databases -- one thousand,
off-the-shelf PC processors are more powerful and much less expensive
than a large mainframe computer [19]. Storage and processing can be
aggregated to achieve massive scale. Wasteful partitioning between
servers or clusters can be avoided. As Gedik and Liu put it, if P2P
is to find its way into applications other than casual file sharing,
then reliability needs to be addressed [20].
The taxonomy of Figure 1 divides the entire body of P2P research
literature along four lines: search, storage, security, and
applications. This survey concentrates on search aspects. A P2P
search network consists of an underlying index (Sections 2 to 4) and
queries that propagate over that index (Section 5).
Search [18, 21-29]
Semantic-Free Indexes [2, 6, 7, 30-52]
Plaxton Trees
Rings
Tori
Butterflies
de Bruijn Graphs
Skip Graphs
Semantic Indexes [4, 53-71]
Keyword Lookup
Peer Information Retrieval
Peer Data Management
Queries [20, 22, 23, 25, 32, 38, 41, 56, 72-100]
Range Queries
Multi-Attribute Queries
Join Queries
Aggregation Queries
Continuous Queries
Recursive Queries
Adaptive Queries
Storage
Consistency & Replication [101-112]
Eventual consistency
Trade-offs
Distribution [39, 42, 90, 92, 113-131]
Epidemics, Bloom Filters
Fault Tolerance [40, 105, 132-139]
Erasure Coding
Byzantine Agreement
Locality [24, 43, 47, 140-160]
Load Balancing [37, 86, 100, 107, 151, 161-171]
Security
Character [172-182]
Identity
Reputation and Trust
Incentives
Goals [25, 27, 71, 183-197]
Availability
Authenticity
Anonymity
Access Control
Fair Trading
Applications [1, 198-200]
Memory [32, 90, 142, 201-222]
File Systems
Web
Content Delivery Networks
Directories
Service Discovery
Publish / Subscribe ...
Intelligence [223-228]
GRID
Security...
Communication [12, 92, 119, 229-247]
Multicasting
Streaming Media
Mobility
Sensors...
Figure 1: Classification of P2P Research Literature
This survey is concerned with two questions. The first, "How do P2P
search networks work?" This foundation is important given the pace
and breadth of P2P research in the last five years. In Section 2, we
classify indexes as local, centralized and distributed. Since
distributed indexes are becoming dominant, they are given closer
attention in Sections 3 and 4. Section 3 compares distributed P2P
indexes for simple key lookup; in particular, their origins (Section
3.1), dependability (Section 3.2), latency (Section 3.3), and their
support for multicast (Section 3.4). It classifies those indexes
according to their routing geometry (Section 3.5) -- Plaxton trees,
rings, tori, butterflies, de Bruijn graphs and skip graphs. Section
4 reviews distributed P2P indexes supporting keyword lookup (Section
4.1) and information retrieval (Section 4.2). Section 5 probes the
embryonic research on P2P queries; in particular, range queries
(Section 5.1), multi-attribute queries (Section 5.2), join queries
(Section 5.3), and aggregation queries (Section 5.4).
The second question, "How robust are P2P search networks?" Insofar
as it is available in the research literature, we tease out the
robustness mechanisms and metrics throughout Sections 2 to 5.
Unfortunately, robustness is often more sensitive to low-level design
choices than it is to the broad P2P index structure, yet these
underlying design choices are seldom isolated in the primary
literature [248]. Furthermore, there has been little consensus on
P2P robustness metrics (Section 3.2). Section 8 gives
recommendations to address these important gaps.
1.1. Related Disciplines
Peer-to-peer research draws upon numerous distributed systems
disciplines. Networking researchers will recognize familiar issues
of naming, routing, and congestion control. P2P designs need to
address routing and security issues across network region boundaries
[152]. Networking research has traditionally been host-centric. The
Web's Universal Resource Identifiers are naturally tied to specific
hosts, making object mobility a challenge [216].
P2P work is data-centric [249]. P2P systems for dynamic object
location and routing have borrowed heavily from the distributed
systems corpus. Some have used replication, erasure codes, and
Byzantine agreement [111]. Others have used epidemics for durable
peer group communication [39].
Similarly, P2P research is set to benefit from database research
[250]. Database researchers will recognize the need to reapply
Codd's principle of physical data independence, that is, to decouple
data indexes from the applications that use the data [23]. It was
the invention of appropriate indexing mechanisms and query
optimizations that enabled data independence. Database indexes like
B+ trees have an analog in P2P's distributed hash tables (DHTs).
Wide-area, P2P query optimization is a ripe, but challenging, area
for innovation.
More flexible distribution of objects comes with increased security
risks. There are opportunities for security researchers to deliver
new methods for availability, file authenticity, anonymity, and
access control [25]. Proactive and reactive mechanisms are needed to
deal with large numbers of autonomous, distributed peers. To build
robust systems from cooperating but self-interested peers, issues of
identity, reputation, trust, and incentives need to be tackled.
Although it is beyond the scope of this paper, robustness against
malicious attacks also ought to be addressed [195].
Possibly the largest portion of P2P research has majored on basic
routing structures [18], where research on algorithms comes to the
fore. Should the overlay be "structured" or "unstructured"? Are the
two approaches competing or complementary? Comparisons of the
"structured" approaches (hypercubes, rings, toroids, butterflies, de
Bruijn, and skip graphs) have weighed the amount of routing state per
peer and the number of links per peer against overlay hop counts.
While "unstructured" overlays initially used blind flooding and
random walks, overheads usually trigger some structure, for example,
super-peers and clusters.
P2P applications rely on cooperation between these disciplines.
Applications have included file sharing, directories, content
delivery networks, email, distributed computation, publish-subscribe
middleware, multicasting, and distributed authentication. Which
applications will be suited to which structures? Are there adaptable
mechanisms that can decouple applications from the underlying data
structures? What are the criteria for selection of applications
amenable to a P2P design [1]?
Robustness is emphasized throughout the survey. We are particularly
interested in two aspects. The first, dependability, was a leading
design goal for the original Internet [251]. It deserves the same
status in P2P. The measures of dependability are well established:
reliability, a measure of the mean-time-to-failure (MTTF);
availability, a measure of both the MTTF and the mean-time-to-repair
(MTTR); maintainability; and safety [252]. The second aspect is the
ability to accommodate variation in outcome, which one could call
adaptability. Its measures have yet to be defined. In the context
of the Internet, it was only recently acknowledged as a first-class
requirement [253]. In P2P, it means planning for the tussles over
resources and identity. It means handling different kinds of queries
and accommodating changeable application requirements with minimal
intervention. It means "organic scaling" [22], whereby the system
grows gracefully, without a priori data center costs or architectural
breakpoints.
In the following section, we discuss one notable omission from the
taxonomy of P2P networking in Figure 1 -- routing.
1.2. Structured and Unstructured Routing
P2P routing algorithms have been classified as "structured" or
"unstructured". Peers in unstructured overlay networks join by
connecting to any existing peers [254]. In structured overlays, the
identifier of the joining peer determines the set of peers that it
connects to [254]. Early instantiations of Gnutella were
unstructured -- keyword queries were flooded widely [255]. Napster
[256] had decentralized content and a centralized index, so it only
partially satisfies the distributed control criteria for P2P systems.
Early structured algorithms included Plaxton, Rajaraman and Richa
(PRR) [30], Pastry [2], Tapestry [31], Chord [7], and the Content
Addressable Network [8]. Mishchke and Stiller recently classified
P2P systems by the presence or absence of structure in routing tables
and network topology [257].
Some have cast unstructured and structured algorithms as competing
alternatives. Unstructured approaches have been called "first
generation", implicitly inferior to the "second generation"
structured algorithms [2, 31]. When generic key lookups are
required, these structured, key-based routing schemes can guarantee
location of a target within a bounded number of hops [23]. The
broadcasting unstructured approaches, however, may have large routing
costs, or fail to find available content [22]. Despite the apparent
advantages of structured P2P, several research groups are still
pursuing unstructured P2P.
There have been two main criticisms of structured systems [61]. The
first relates to peer transience, which in turn, affects robustness.
Chawathe, et al. opined that highly transient peers are not well
supported by DHTs [61]. P2P systems often exhibit "churn", with
peers continually arriving and departing. One objection to concerns
about highly transient peers is that many applications use peers in
well-connected parts of the network. The Tapestry authors analyzed
the impact of churn in a network of 1000 nodes [31]. Others opined
that it is possible to maintain a robust DHT at relatively low cost
[258]. Very few papers have quantitatively compared the resilience
of structured systems. Loguinov, Kumar, et al. claimed that there
were only two such works [24, 36].
The second criticism of structured systems is that they do not
support keyword searches and complex queries as well as unstructured
systems. Given the current file-sharing deployments, keyword
searches seem more important than exact-match key searches in the
short term. Paraphrased, "most queries are for hay, not needles"
[61].
More recently, some have justifiably seen unstructured and structured
proposals as complementary, and have devised hybrid models [259].
Their starting point was the observation that unstructured flooding
or random walks are inefficient for data that is not highly
replicated across the P2P network. Structured graphs can find keys
efficiently, irrespective of replication. Castro, et al. proposed
Structella, a hybrid of Gnutella built on top of Pastry [259].
Another design used structured search for rare items and unstructured
search for massively replicated items [54].
However, the "structured versus unstructured routing" taxonomy is
becoming less useful, for two reasons, Firstly, most "unstructured"
proposals have evolved and incorporated structure. Consider the
classic "unstructured" system, Gnutella [4]. For scalability, its
peers are either ultrapeers or leaf nodes. This hierarchy is
augmented with a query routing protocol whereby ultrapeers receive a
hashed summary of the resource names available at leaf nodes.
Between ultrapeers, simple query broadcast is still used, though
methods to reduce the query load here have been considered [260].
Secondly, there are emerging schema-based P2P designs [59], with
super-node hierarchies and structure within documents. These are
quite distinct from the structured DHT proposals.
1.3. Indexes and Queries
Given that most, if not all, P2P designs today assume some structure,
a more instructive taxonomy would describe the structure. In this
survey, we use a database taxonomy in lieu of the networking
taxonomy, as suggested by Hellerstein, Cooper, and Garcia-Molina [23,
261]. The structure is determined by the type of index (Sections 2 ,
3, and 4). Queries feature in lieu of routing (Section 5). The DHT
algorithms implement a "semantic-free index" [216]. They are
oblivious of whether keys represent document titles, meta-data, or
text. Gnutella-like and schema-based proposals have a "semantic
index".
Index engineering is at the heart of P2P search methods. It captures
a broad range of P2P issues, as demonstrated by the Search/Index
Links model [261]. As Manber put it, "the most important of the
tools for information retrieval is the index -- a collection of terms
with pointers to places where information about documents can be
found" [262]. Sen and Wang noted that a "P2P network" usually
consists of connections between hosts for application-layer
signaling, rather than for the data transfer itself [263].
Similarly, we concentrate on the "signaled" indexes and queries.
Our focus here is the dependability and adaptability of the search
network. Static dependability is a measure of how well queries route
around failures in a network that is normally fault-free. Dynamic
dependability gives an indication of query success when nodes and
data are continually joining and leaving the P2P system. An
adaptable index accommodates change in the data and query
distribution. It enables data independence, in that it facilitates
changes to the data layout without requiring changes to the
applications that use the data [23]. An adaptable P2P system can
support rich queries for a wide range of applications. Some
applications benefit from simple, semantic-free key lookups [264].
Others require more complex, Structured Query Language (SQL)-like
queries to find documents with multiple keywords, or to aggregate or
join query results from distributed relations [22].
2. Index Types
A P2P index can be local, centralized, or distributed. With a local
index, a peer only keeps the references to its own data, and does not
receive references for data at other nodes. The very early Gnutella
design epitomized the local index (Section 2.1). In a centralized
index, a single server keeps references to data on many peers. The
classic example is Napster (Section 2.2). With distributed indexes,
pointers towards the target reside at several nodes. One very early
example is Freenet (Section 2.3). Distributed indexes are used in
most P2P designs nowadays -- they dominate this survey.
P2P indexes can also be classified as non-forwarding and forwarding.
When queries are guided by a non-forwarding index, they jump to the
node containing the target data in a single hop. There have been
semantic and semantic-free one-hop schemes [138, 265, 266]. Where
scalability to a massive number of peers is required, these schemes
have been extended to two hops [267, 268]. More common are the
forwarding P2Ps, where the number of hops varies with the total
number of peers, often logarithmically. The related trade-offs
between routing state, lookup latency, update bandwidth, and peer
churn are critical to total system dependability.
2.1. Local Index (Gnutella)
P2Ps with a purely local data index are becoming rare. In such
designs, peers flood queries widely and only index their own content.
They enable rich queries - the search is not limited to a simple key
lookup. However, they also generate a large volume of query traffic
with no guarantee that a match will be found, even if it does exist
on the network. For example, to find potential peers on the early
instantiations of Gnutella, 'ping' messages were broadcast over the
P2P network and the 'pong' responses were used to build the node
index. Then, small 'query' messages, each with a list of keywords,
are broadcast to peers that respond with matching filenames [4].
There have been numerous attempts to improve the scalability of
local-index P2P networks. Gnutella uses fixed time-to-live (TTL)
rings, where the query's TTL is set less than 7-10 hops [4]. Small
TTLs reduce the network traffic and the load on peers, but also
reduce the chances of a successful query hit. One paper reported,
perhaps a little too bluntly, that the fixed "TTL-based mechanism
does not work" [67]. To address this TTL selection problem, they
proposed an expanding ring, known elsewhere as iterative deepening
[29]. It uses successively larger TTL counters until there is a
match. The flooding, ring, and expanding ring methods all increase
network load with duplicated query messages. A random walk, whereby
an unduplicated query wanders about the network, does indeed reduce
the network load but massively increases the search latency. One
solution is to replicate the query k times at each peer. Called
random k-walkers, this technique can be coupled with TTL limits, or
periodic checks with the query originator, to cap the query load
[67]. Adamic, Lukose, et al. suggested that the random walk searches
be directed to nodes with a higher degree, that is, with larger
numbers of inter-peer connections [269]. They assumed that higher-
degree peers are also capable of higher query throughputs. However,
without some balancing design rule, such peers would be swamped with
the entire P2P signaling traffic. In addition to the above
approaches, there is the 'directed breadth-first' algorithm [29]. It
forwards queries within a subset of peers selected according to
heuristics on previous performance, like the number of successful
query results. Another algorithm, called probabilistic flooding, has
been modeled using percolation theory [270].
Several measurement studies have investigated locally indexed P2Ps.
Jovanovic noted Gnutella's power law behaviour [70]. Sen and Wang
compared the performance of Gnutella, Fasttrack [271], and Direct
Connect [263, 272, 273]. At the time, only Gnutella used local data
indexes. All three schemes now use distributed data indexes, with
hierarchy in the form of Ultrapeers (Gnutella), Super-Nodes
FastTrack), and Hubs (Direct Connect). It was found that a very
small percentage of peers have a very high degree and that the total
system dependability is at the mercy of such peers. While peer up-
time and bandwidth were heavy-tailed, they did not fit well with the
Zipf distribution. Fortunately for Internet Service Providers,
measures aggregated by IP prefix and Autonomous System (AS) were more
stable than for individual IP addresses. A study of University of
Washington traffic found that Gnutella and Kazaa together contributed
43% of the university's total TCP traffic [274]. They also reported
a heavy-tailed distribution, with 600 external peers (out of 281,026)
delivering 26% of Kazaa bytes to internal peers. Furthermore,
objects retrieved from the P2P network were typically three orders of
magnitude larger than Web objects -- 300 objects contributed to
almost half the total outbound Kazaa bandwidth. Others reported
Gnutella's topology mismatch, whereby only 2-5% of P2P connections
link peers in the same Autonomous System (AS), despite over 40% of
peers being in the top 10 ASs [65]. Together these studies
underscore the significance of multimedia sharing applications. They
motivate interesting caching and locality solutions to the topology
mismatch problem.
These same studies bear out one main dependability lesson: total
system dependability may be sensitive to the dependability of high-
degree peers. The designers of Scamp translated this observation to
the design heuristic, "have the degree of each node be of nearly
equal size" [153]. They analyzed a system of N peers, with mean
degree c.log(n), where link failures occur independently with
probability e. If d>0 is fixed and c>(1+d)/(-log(e)), then the
probability of graph disconnection goes to zero as N->infinity.
Otherwise, if c<(1-d)/(-log(e)), then the probability of
disconnection goes to one as N->infinity. They presented a
localizer, which finds approximate minima to a global function of
peer degree and arbitrary link costs using only local information.
The Scamp overlay construction algorithms could support any of the
flooding and walking routing schemes above, or other epidemic and
multicasting schemes for that matter. Resilience to high churn rates
was identified for future study.
2.2. Central Index (Napster)
Centralized schemes like Napster [256] are significant because they
were the first to demonstrate the P2P scalability that comes from
separating the data index from the data itself. Ultimately, 36
million Napster users lost their service not because of technical
failure, but because the single administration was vulnerable to the
legal challenges of record companies [275].
There has since been little research on P2P systems with central data
indexes. Such systems have also been called 'hybrid' since the index
is centralized but the data is distributed. Yang and Garcia-Molina
devised a four-way classification of hybrid systems [276]: unchained
servers, where users whose index is on one server do not see other
servers' indexes; chained servers, where the server that receives a
query forwards it to a list of servers if it does not own the index
itself; full replication, where all centralized servers keep a
complete index of all available metadata; and hashing, where keywords
are hashed to the server where the associated inverted list is kept.
The unchained architecture was used by Napster, but it has the
disadvantage that users do not see all indexed data in the system.
Strictly speaking, the other three options illustrate the distributed
data index, not the central index. The chained architecture was
recommended as the optimum for the music-swapping application at the
time. The methods by which clients update the central index were
classified as batch or incremental, with the optimum determined by
the query-to-login ratio. Measurements were derived from a clone of
Napster called OpenNap[277]. Another study of live Napster data
reported wide variation in the availability of peers, a general
unwillingness to share files (20-40% of peers share few or no files),
and a common understatement of available bandwidth so as to
discourage other peers from sharing one's link [202].
Influenced by Napster's early demise, the P2P research community may
have prematurely turned its back on centralized architectures.
Chawathe, Ratnasamy, et al. opined that Google and Yahoo demonstrate
the viability of a centralized index. They argued that "the real
barriers to Napster-like designs are not technical but legal and
financial" [61]. Even this view may be a little too harsh on the
centralized architectures -- it implies that they always have an up-
front capital hurdle that is steeper than for distributed
architectures. The closer one looks at scalable 'centralized'
architectures, the less the distinction with 'distributed'
architectures seems to matter. For example, it is clear that
Google's designers consider Google a distributed, not centralized,
file system [278]. Google demonstrates the scale and performance
possible on commodity hardware, but still has a centralized master
that is critical to the operation of each Google cluster. Time may
prove that the value of emerging P2P networks, regardless of the
centralized-versus-distributed classification, is that they smooth
the capital outlays and remove the single points of failure across
the spectra of scale and geographic distribution.
2.3. Distributed Index (Freenet)
An important early P2P proposal for a distributed index was Freenet
[5, 71, 279]. While its primary emphasis was the anonymity of peers,
it did introduce a novel indexing scheme. Files are identified by
low-level "content-hash" keys and by "secure signed-subspace" keys,
which ensure that only a file owner can write to a file while anyone
can read from it. To find a file, the requesting peer first checks
its local table for the node with keys closest to the target. When
that node receives the query, it too checks for either a match or
another node with keys close to the target. Eventually, the query
either finds the target or exceeds time-to-live (TTL) limits. The
query response traverses the successful query path in reverse,
depositing a new routing table entry (the requested key and the data
holder) at each peer. The insert message similarly steps towards the
target node, updating routing table entries as it goes, and finally
stores the file there. Whereas early versions of Gnutella used
breadth-first flooding, Freenet uses a more economic depth-first
search [280].
An initial assessment has been done of Freenet's robustness. It was
shown that in a network of 1000 nodes, the median query path length
stayed under 20 hops for a failure of 30% of nodes. While the
Freenet designers considered this as evidence that the system is
"surprisingly robust against quite large failures" [71], the same
datapoint may well be outside meaningful operating bounds. How many
applications are useful when the first quartile of queries have path
lengths of several hundred hops in a network of only 1000 nodes, per
Figure 4 of [71]? To date, there has been no analysis of Freenet's
dynamic robustness. For example, how does it perform when nodes are
continually arriving and departing?
There have been both criticisms and extensions of the early Freenet
work. Gnutella proponents acknowledged the merit in Freenet's
avoidance of query broadcasting [281]. However, they are critical on
two counts: the exact file name is needed to construct a query; and
exactly one match is returned for each query. P2P designs using
DHTs, per Section 3, share similar characteristics -- a precise query
yields a precise response. The similarity is not surprising since
Freenet also uses a hash function to generate keys. However, the
query routing used in the DHTs has firmer theoretical foundations.
Another difference with DHTs is that Freenet will take time, when a
new node joins the network, to build an index that facilitates
efficient query routing. By the inventor's own admission, this is
damaging for a user's first impressions [282]. It was proposed to
download a copy of routing tables from seed nodes at startup, even
though the new node might be far from the seed node. Freenet's slow
startup motivated Mache, Gilbert, et al. to amend the overlay after
failed requests and to place additional index entries on successful
requests -- they claim almost an order of magnitude reduction in
average query path length [280]. Clarke also highlighted the lack of
locality or bandwidth information available for efficient query
routing decisions [282]. He proposed that each node gather response
times, connection times, and proportion of successful requests for
each entry in the query routing table. When searching for a key that
is not in its own routing table, it was proposed to estimate response
times from the routing metrics for the nearest known keys and
consequently choose the node that can retrieve the data fastest. The
response time heuristic assumed that nodes close in the key space
have similar response times. This assumption stemmed from early
deployment observations that Freenet peers seemed to specialize in
parts of the keyspace -- it has not been justified analytically.
Kronfol drew attention to Freenet's inability to do keyword searches
[283]. He suggested that peers cache lists of weighted keywords in
order to route queries to documents, using Term Frequency Inverse
Document Frequency (TFIDF) measures and inverted indexes (Section
4.2.1). With these methods, a peer can route queries for simple
keyword lists or more complicated conjunctions and disjunctions of
keywords. Robustness analysis and simulation of Kronfol's proposal
remain open.
The vast majority of P2P proposals in following sections rely on a
distributed index.
3. Semantic Free Index
Many of today's distributed network indexes are semantic. The
semantic index is human-readable. For example, it might associate
information with other keywords, a document, a database key, or even
an administrative domain. It makes it easy to associate objects with
particular network providers, companies, or organizations, as
evidenced in the Domain Name System (DNS). However, it can also
trigger legal tussles and frustrate content replication and migration
[216].
Distributed Hash Tables (DHTs) have been proposed to provide
semantic-free, data-centric references. DHTs enable one to find an
object's persistent key in a very large, changing set of hosts. They
are typically designed for [23]:
a) low degree. If each node keeps routing information for only a
small number of other nodes, the impact of high node arrival and
departure rates is contained;
b) low hop count. The hops and delay introduced by the extra
indirection are minimized;
c) greedy routing. Nodes independently calculate a short path to the
target. At each hop, the query moves closer to the target; and
d) robustness. A path to the target can be found even when links or
nodes fail.
3.1. Origins
To understand the origins of recent DHTs, one needs to look to three
contributions from the 1990s. The first two -- Plaxton, Rajaraman,
and Richa (PRR) [30] and Consistent Hashing [49] -- were published
within one month of each other. The third, the Scalable Distributed
Data Structure (SDDS) [52], was curiously ignored in significant
structured P2P designs despite having some similar goals [2, 6, 7].
It has been briefly referenced in other P2P papers [46, 284-287].
3.1.1. Plaxton, Rajaraman, and Richa (PRR)
PRR is the most recent of the three. It influenced the designs of
Pastry [2], Tapestry [6], and Chord [7]. The value of PRR is that it
can locate objects using fixed-length routing tables [6]. Objects
and nodes are assigned a semantic-free address, for example a 160-bit
key. Every node is effectively the root of a spanning tree. A
message routes toward an object by matching longer address suffixes,
until it encounters either the object's root node or another node
with a 'nearby' copy. It can route around link and node failure by
matching nodes with a related suffix. The scheme has several
disadvantages [6]: global knowledge is needed to construct the
overlay; an object's root node is a single point of failure; nodes
cannot be inserted and deleted; and there is no mechanism for queries
to avoid congestion hot spots.
3.1.2. Consistent Hashing
Consistent Hashing [288] strongly influenced the designs of Chord [7]
and Koorde [37]. Karger, et al. introduced Consistent Hashing in the
context of the Web-caching problem [49]. Web servers could
conceivably use standard hashing to place objects across a network of
caches. Clients could use the approach to find the objects. For
normal hashing, most object references would be moved when caches are
added or deleted. On the other hand, Consistent Hashing is "smooth"
-- when caches are added or deleted, the minimum number of object
references move so as to maintain load balancing. Consistent Hashing
also ensures that the total number of caches responsible for a
particular object is limited. Whereas Litwin's Linear Hashing (LH*)
scheme requires 'buckets' to be added one at a time in sequence [50],
Consistent Hashing allows them to be added in any order [49]. There
is an open Consistent Hashing problem pertaining to the fraction of
items moved when a node is inserted [165]. Extended Consistent
Hashing was recently proposed to randomize queries over the spread of
caches to significantly reduce the load variance [289].
Interestingly, Karger [49] referred to an older DHT algorithm by
Devine that used "a novel autonomous location discovery algorithm
that learns the buckets' locations instead of using a centralized
directory" [51].
3.1.3. Scalable Distributed Data Structures (LH*)
In turn, Devine's primary point of reference was Litwin's work on
SDDSs and the associated LH* algorithm [52]. An SDDS satisfies three
design requirements: files grow to new servers only when existing
servers are well loaded; there is no centralized directory; and the
basic operations like insert, search, and split never require atomic
updates to multiple clients. Honicky and Miller suggested the first
requirement could be considered a limitation since expansion to new
servers is not under administrative control [286]. Litwin recently
noted numerous similarities and differences between LH* and Chord
[290]. He found that both implement key search. Although LH* refers
to clients and servers, nodes can operate as peers in both. Chord
'splits' nodes when a new node is inserted, while LH* schedules
'splits' to avoid overload. Chord requests travel O(log n) hops,
while LH* client requests need, at most, two hops to find the target.
Chord stores a small number of 'fingers' at each node. LH* servers
store N/2 to N addresses while LH* clients store 1 to N addresses.
This trade-off between hop count and the size of the index affects
system robustness, and bears striking similarity to recent one- and
two-hop P2P schemes in Section 2. The arrival and departure of LH*
clients does not disrupt LH* server metadata at all. Given the size
of the index, the arrival and departure of LH* servers are likely to
cause more churn than that of Chord nodes. Unlike Chord, LH* has a
single point of failure, the split coordinator. It can be
replicated. Alternatively, it can be removed in later LH* variants,
though details have not been progressed for lack of practical need
[290].
3.2. Dependability
We make four overall observations about their dependability.
Dependability metrics fall into two categories: static dependability,
a measure of performance before recovery mechanisms take over; and
dynamic dependability, for the most likely case in massive networks
where there is continual failure and recovery ("churn").
3.2.1. Static Dependability
Observation A: Static dependability comparisons show that no O(log n)
DHT geometry is significantly more dependable than the other O(log n)
geometries.
Gummadi, et al. compared the tree, hypercube, butterfly, ring, XOR,
and hybrid geometries. In such geometries, nodes generally know
about O(log n) neighbors and route to a destination in O(log n) hops,
where N is the number of nodes in the overlay. Gummadi, et al. asked
"Why not the ring?" They concluded that only the ring and XOR
geometries permit flexible choice of both neighbors and alternative
routes [24]. Loguinov, et al. added the de Bruijn graph to their
comparison [36]. They concluded that the classical analyses, for
example the probability that a particular node becomes disconnected,
yield no major differences between the resilience of Chord, CAN, and
de Bruijn graphs. Using bisection width (the minimum edge count
between two equal partitions) and path overlap (the likelihood that
backup paths will encounter the same failed nodes or links as the
primary path), they argued for the superior resilience of the de
Bruijn graph. In short, ring, XOR, and de Bruijn graphs all permit
flexible choice of alternative paths, but only in de Bruijn are the
alternate paths independent of each other [36].
3.2.2. Dynamic Dependability
Observation B: Dynamic dependability comparisons show that DHT
dependability is sensitive to the underlying topology maintenance
algorithms.
Li, et al. give the best comparison to date of several leading DHTs
during churn [291]. They relate the disparate configuration
parameters of Tapestry, Chord, Kademlia, Kelips, and OneHop to
fundamental design choices. For each of these DHTs, they plotted the
optimal performance in terms of lookup latency (milliseconds) and
fraction of failed lookups. The results led to several important
insights about the underlying algorithms, for example: increasing
routing table size is more cost-effective than increasing the rate of
periodic stabilization; learning about new nodes during the lookup
process sometimes eliminates the need for stabilization; and parallel
lookups reduce latency due to timeouts more effectively than faster
stabilization. Similarly, Zhuang, et al. compared keep-alive
algorithms for DHT failure detection [292]. Such algorithmic
comparisons can significantly improve the dependability of DHT
designs.
In Figure 2, we propose a taxonomy for the topology maintenance
algorithms that influence dependability. The algorithms can be
classified by how nodes join and leave, how they first detect
failures, how they share information about topology updates, and how
they react when they receive information about topology updates.
Normal Updates
Joins (passive; active) [293]
Leaves (passive; active) [293]
Fault Detection [292]
Maintenance
Proactive (periodic or keep-alive probes)
Reactive (correction-on-use, correction-on-failure) [294]
Report
Negative (all dead nodes, nodes recently failed)
Positive (all live nodes; nodes recently recovered) [292]
Topology Sharing: yes/ no [292]
Multicast Tree (explicit, implicit) [267, 295]
Gossip (timeouts; number of contacts) [39]
Corrective Action
Routing
Rerouting actions
(reroute once; route in parallel [291]; reject)
Routing timeouts
(TCP-style, virtual coordinates) [296]
Topology
Update action (evict/ replace/ tag node)
Update timeliness (immediate, periodic[296], delayed [297])
Figure 2: Topology Maintenance in Distributed Hash Tables
3.2.3. Ephemeral or Stable Nodes -- O(log n) or O(1) Hops
Observation C: Most DHTs use O(log n) geometries to suit ephemeral
nodes. The O(1) hop DHTs suit stable nodes and deserve more research
attention.
Most of the DHTs in Section 3.5 assume that nodes are ephemeral, with
expected lifetimes of one to two hours. Therefore, they mostly use
an O(log n) geometry. The common assumption is that maintenance of
full routing tables in the O(1) hop DHTs will consume excessive
bandwidth when nodes are continually joining and leaving. The
corollary is that, when they run on stable infrastructure servers
[298], most of the DHTs in Section 3.5 are less than optimal --
lookups take many more hops than necessary, wasting latency and
bandwidth budgets. The O(1) hop DHTs suit stable deployments and
high lookup rates. For a churning 1024-node network, Li, et al.
concluded that OneHop is superior to Chord, Tapestry, Kademlia, and
Kelips in terms of latency and lookup success rate [291]. For a
3000-node network, they concluded that "OneHop is only preferable to
Chord when the deployment scenario allows a communication cost
greater than 20 bytes per node per second" [291]. This apparent
limitation needs to be put in context. They assumed that each node
issues only one lookup every 10 minutes and has a lifetime of only 60
minutes. It seems reasonable to expect that in some deployments,
nodes will have a lifetime of weeks or more, a maintenance bandwidth
of tens of kilobits per second, and a load of hundreds of lookups per
second. O(1) hop DHTs are superior in such situations. OneHop can
scale at least to many tens of thousands of nodes [267]. The recent
O(1) hop designs [267, 295] are vastly outnumbered by the O(log n)
DHTs in Section 3.5. Research on the algorithms of Figure 2 will
also yield improvements in the dependability of the O(1) hop DHTs.
3.2.4. Simulation and Proof
Observation D: Although not yet a mature science, the study of DHT
dependability is helped by recent simulation and formal development
tools.
While there are recent reference architectures [294, 298], much of
the DHT literature in Section 3.5 does not lend itself to repeatable,
comparative studies. The best comparative work to date [291] relies
on the Peer-to-Peer Simulator (P2PSIM) [299]. At the time of
writing, it supports more DHT geometries than any other simulator.
As the study of DHTs matures, we can expect to see the simulation
emphasis shift from geometric comparison to a comparison of the
algorithms of Figure 2.
P2P correctness proofs generally rely on less-than-complete formal
specifications of system invariants and events [7, 45, 300]. Li and
Plaxton expressed concern that "when many joins and leaves happen
concurrently, it is not clear whether the neighbor tables will remain
in a 'good' state" [47]. While acknowledging that guaranteeing
consistency in a failure-prone network is impossible, Lynch, Malkhi,
et al. sketched amendments to the Chord algorithm to guarantee
atomicity [301]. More recently, Gilbert, Lynch, et al. gave a new
algorithm for atomic read/write memory in a churning distributed
network, suggesting it to be a good match for P2P [302]. Lynch and
Stoica show in an enhancement to Chord that lookups are provably
correct when there is a limited rate of joins and failures [303].
Fault Tolerant Active Rings is a protocol for active joins and leaves
that was formally specified and proven using B-method tools [304]. A
good starting point for a formal DHT development would be the
numerous informal API specifications [22, 305, 306]. Such work could
be informed by other efforts to formally specify routing invariants
[307, 308].
3.3. Latency
The key metrics for DHT latency are:
1) Shortest-Path Distance and Diameter. In graph theory, the
shortest-path distance is the minimum number of edges in any path
between two vertices of the graph. Diameter is the largest of all
shortest-path distances in a graph [309]. Networking synonyms for
distance on a DHT are "hop count" and "lookup length".
2) Latency and Latency Stretch. Two types of latency are relevant
here -- network-layer latency and overlay latency. Network-layer
latency has been referred to as "proximity" or "locality" [24].
Stretch is the cost of an overlay path between two nodes, divided
by the cost of the direct network path between those nodes [310].
Latency stretch is also known as the "relative delay penalty"
[311].
3.3.1. Hop Count and the O(1)-Hop DHTs
Hop count gives an approximate indication of path latency. O(1)-hop
DHTs have path latencies lower than the O(log n)-hop DHTs [291].
This significant advantage is often overlooked on account of concern
about the messaging costs to maintain large routing tables (Section
3.2.3). Such concern is justified when the mean node lifetime is
only a few hours and the mean lookup interval per node is more than a
few seconds (the classic profile of a P2P file-sharing node).
However, for a large, practical operating range (node lifetimes of
days or more, lookup rates of over tens of lookups per second per
node, up to ~100,000 nodes), the total messaging cost in O(1) hop
DHTs is lower than in O(log n) DHTs [312]. Lookups and routing table
maintenance contribute to the total messaging cost. If a deployment
fits this operating range, then O(1)-hop DHTs will give lower path
latencies and lower total messaging costs. An additional merit of
the O(1)-hop DHTs is that they yield lower lookup failure rates than
their O(log N)-hop counterparts [291].
Low hop count can be achieved in two ways: each node has a large O(N)
index of nodes; or the object references can be replicated on many
nodes. Beehive [313], Kelips [39], LAND [310], and Tulip [314] are
examples of the latter category. Beehive achieves O(1) hops on
average and O(log n) hops in the worst case, by proactive replication
of popular objects. Kelips replicates the 'file index'. It incurs
O(sqrt(N)) storage costs for both the node index and the file index.
LAND uses O(log n) reference pointers for each stored object and an
O(log n) index to achieve a worst-case 1+e stretch, where 0<e. The
Kelips-like Tulip [314] requires 2 hops per lookup. Each node
maintains 2sqrt(N)log(N) links to other nodes and objects are
replicated on O(sqrt(N)) nodes.
The DHTs with a large O(N) node index can be divided into two groups:
those for which the index is always O(N); and those for which the
index opportunistically ranges from O(log n) to O(N). Linear Hashing
(LH*) servers [52], OneHop [267], and 1h-Calot [295] fall into the
former category. EpiChord [315] and Accordion [316] are examples of
the latter.
3.3.2. Proximity and the O(log n)-Hop DHTs
If one chooses not to use single-hop DHTs, hop count is a weak
indicator of end-to-end path latency. Some hops may incur large
delays because of intercontinental or satellite links. Consequently,
numerous DHT designs minimize path latency by considering the
proximity of nodes. Gummadi, et al. classified the proximity methods
as follows [24]:
1) Proximity Neighbor Selection (PNS). The nodes in the routing
table are chosen based on the latency of the direct hop to those
nodes. The latency may be explicitly measured [317], or it may be
estimated using one of several synthetic coordinate systems [150,
154, 318]. As a lower bound on PNS performance, Dabek, et al.
showed that lookups on O(log n) DHTs take at least 1.5 times the
average roundtrip time of the underlying network [154].
2) Proximity Route Selection (PRS). At lookup time, the choice of
the next-hop node relies on the latency of the direct hop to that
node. PRS is less effective than PNS, though it may complement it
[24]. Some of the routing geometries in Section 3.5 do not
support PNS and/or PRS [24].
3) Proximity Identifier Selection (PIS). Node identifiers indicate
geographic position. PIS frustrates load balancing, increases the
risk of correlated failures, and is not often used [24].
The proximity study by Gummadi, et al. assumed recursive routing,
though they suggested that PNS would also be superior to PRS with
iterative routing [24]. Dabek, et al. found that recursive lookups
take 0.6 times as long as iterative lookups [150].
Beyond the explicit use of proximity information, redundancy can help
to avoid slow paths and servers. One may increase the number of
replicas [150], use parallel lookups [291, 316], use alternate routes
on failure [150], or use multiple gateway nodes to enter the DHT
[317].
3.4. Multicasting
3.4.1. Multicasting vs. Broadcasting
"Multicasting" here means sending a message to a subset of an
overlay's nodes. Nodes explicitly join and leave this subset, called
a "multicast group". "Broadcasting" here is a special case of
multicasting in which a message is sent to all nodes in the overlay.
Broadcasting relies on overlay membership messages -- it does not
need extra group membership messaging. Castro, et al. said
multicasting on structured overlays is either "flooding" (one overlay
per group) or "tree-based" (one tree per group) [319]. These are
synonyms for broadcasting and multicasting respectively.
The first DHT-based designs for multicasting were CAN multicast
[320], Scribe [241], Bayeux [242], and i3 [231]. They were based on
CAN [8], Pastry [2], Tapestry [31], and Chord [7] respectively. El-
Ansary, et al. devised the first DHT-based broadcasting scheme [321].
It was based on Chord.
Multicast trees can be constructed using reverse-path forwarding or
forward-path forwarding. Scribe uses reverse-path forwarding [241].
Bayeux uses forward-path forwarding [242]. Borg, a multicast design
based on Pastry, uses a combination of forward-path and reverse-path
forwarding to minimize latency [237].
3.4.2. Motivation for DHT-based Multicasting
Multicasting complements DHT search capability. DHTs naturally
support exact match queries. With multicasting, they can support
more complex queries. Multicasting also enables the dissemination
and collection of global information.
Consider, for example, aggregation queries like minimum, maximum,
count, sum, and average (Section 5.4). A node at the root of a
dissemination tree might multicast such a query [322]. The leaf
nodes return local results towards the root node. Successive parents
aggregate the result so that eventually the root node can compute the
global result. Such queries may help to monitor the capacity and
health of the overlay itself.
Why bother with structured overlays for multicasting? In Section
2.1, we saw that Gnutella can multicast complex queries without them
[4]. Castro, et al. posed the question, "Should we build Gnutella on
a structured overlay?" [259]. While acknowledging that their study
was preliminary, they did conclude that "we see no reason to build
Gnutella on top of an unstructured overlay" [259]. The supposedly
high maintenance costs of structured overlays were outweighed by
query cost savings. The structured overlay ensured that nodes were
only visited once during a complex query. It also helped to
accurately limit the total number of nodes visited. Pai, et al.
acknowledged that multicast trees based on structured overlays
contribute to simple routing rules, low delay and low delay variation
[323]. However, they opted for unstructured, gossip-based
multicasting for reliability reasons: data loss near the tree root
affects all subtended nodes; interior node failures must be repaired
quickly; interior nodes are obliged to disseminate more than their
fair share of traffic, giving leaf nodes a "free ride". The most
promising research direction is to improve on the Bimodal
Multicasting approach [324]. It combines the bandwidth efficiency
and low latency of structured, best-effort multicasting trees with
the reliability of unstructured gossip protocols.
3.4.3. Design Issues
None of the early structured overlay multicast designs addressed all
of the following issues [325]:
1) Heterogeneous Node Capacity. Nodes differ in their processing,
memory, and network capacity. Multicast throughput is largely
determined by the node with smallest throughput [325]. To limit
the multicasting load on a node, one might cap its out-degree. If
the same node receives further join requests, it refers them to
its children ("pushdown") [240]. Bharambe, et al. explored
several pushdown strategies but found them inadequate to deal with
heterogeneity [326]. They concluded that the heterogeneity issue
remains open, and should be addressed before deploying DHTs for
high-bandwidth multicasting applications. Independently, Zhang et
al. partially tackled heterogeneity by allowing nodes in their
CAM-Chord and CAM-Koorde designs to vary out-degree according to
the node's capacity [325]. However, they made no mention of the
"pushdown" issue -- they did not describe topology maintenance
when the out-degree limit is reached.
2) Reliability (Dynamic Membership). If a multicast tree is to be
resilient, it must survive dynamic membership. There are several
ways to deal with dynamic membership: ensure that the root node of
the multicasting tree does not handle all requests to join or
leave the multicast group [242]; use multiple interior-node-
disjoint trees to avoid single points of failure in tree
structures [322]; and split the root node into several replicas
and partition members across them [241]. For example, Bayeux
requires the root node to track all group membership changes
whereas Scribe does not [241]. CAN-multicast uses a single,
well-known host to bootstrap the join operations [320]. The
earliest DHT-based broadcasting work by El-Ansary, et al. did not
address the issue of dynamic membership [321]. Ghodsi, et al.
addressed it in a subsequent paper, though, giving two broadcast
algorithms that accommodate routing table inconsistencies [327].
One algorithm achieves a more optimal multicasting network at the
expense of greater correction overhead. Splitstream, based on
Scribe and Pastry, redundantly striped content across multiple
interior-node-disjoint multicast trees -- if one interior node
fails, then only one stripe is lost [240].
3) Large Any-Source Multicast Groups. Any group member should be
allowed to send multicast messages. The group should scale to a
very large number of hosts. CAN-based multicast was the first
application-level multicast scheme to scale to groups of several
thousands of nodes without restricting the service model to a
single source [320]. Bayeux scales to large groups but has a
single root node for each multicast group. It supports the any-
source model only by having the root node operate as a reflector
for multiple senders [242].
3.5. Routing Geometries
In Sections 3.5.1 to 3.5.6, we introduce the main geometries for
simple key lookup and survey their robustness mechanisms.
3.5.1. Plaxton Trees (Pastry, Tapestry)
Work began in March 2000 on a structured, fault-tolerant, wide-area
Dynamic Object Location and Routing (DOLR) system called Tapestry [6,
155]. While DHTs fix replica locations, a DOLR API enables
applications to control object placement [31]. Tapestry's basic
location and routing scheme follows Plaxton, Rajaraman, and Richa
(PRR) [30], but it remedies PRR's robustness shortcomings described
in Section 3.1. Whereas each object has one root node in PRR,
Tapestry uses several to avoid a single point of failure. Unlike
PRR, it allows nodes to be inserted and deleted. Whereas PRR
required a total ordering of nodes, Tapestry uses 'surrogate routing'
to incrementally choose root nodes. The PRR algorithm does not
address congestion, but Tapestry can put object copies close to nodes
generating high query loads. PRR nodes only know of the nearest
replica, whereas Tapestry nodes enable selection from a set of
replicas (for example, to retrieve the most up to date). To detect
routing faults, Tapestry uses TCP timeouts and UDP heartbeats for
detection, sequential secondary neighbours for rerouting, and a
'second chance' window so that recovery can occur without the
overhead of a full node insertion. Tapestry's dependability has been
measured on a testbed of about 100 machines and on simulations of
about 1000 nodes. Successful routing rates and maintenance
bandwidths were measured during instantaneous failures and ongoing
churn [31].
Pastry, like Tapestry, uses Plaxton-like prefix routing [2]. As in
Tapestry, Pastry nodes maintain O(log n) neighbours and route to a
target in O(log n) hops. Pastry differs from Tapestry only in the
method by which it handles network locality and replication [2].
Each Pastry node maintains a 'leaf set' and a 'routing table'. The
leaf set contains l/2 node IDs on either side of the local node ID in
the node ID space. The routing table, in row r, column c, points to
the node ID with the same r-digit prefix as the local node, but with
an r+1 digit of c. A Pastry node periodically probes leaf set and
routing table nodes, with periodicity of Tls and Trt and a timeout
Tout. Mahajan, Castry, et al. analyzed the reliability versus
maintenance cost trade-offs in terms of the parameters l, Tls, Trt,
and Tout [328]. They concluded that earlier concerns about excessive
maintenance cost in a churning P2P network were unfounded, but
suggested follow-up work for a wider range of reliability targets,
maintenance costs, and probe periods. Rhea Geels, et al. concluded
that existing DHTs fail at high churn rates [329]. Building on a
Pastry implementation from Rice University, they found that most
lookups fail to complete when there is excessive churn. They
conjectured that short-lived nodes often leave the network with
lookups that have not yet timed out, but no evidence was provided to
confirm the theory. They identified three design issues that affect
DHT performance under churn: reactive versus periodic recovery of
peers; lookup timeouts; and choice of nearby neighbours. Since
reactive recovery was found to add traffic to already congested
links, the authors used periodic recovery in their design. For
lookup timeouts, they advocated an exponentially weighted moving
average of each neighbour's response time, over alternative fixed
timeout or 'virtual coordinate' schemes. For selection of nearby
neighbours, they found that 'global sampling' was more effective than
simply sampling a 'neighbour's neighbours' or 'inverse neighbours'.
Castro, Costa, et al. have refuted the suggestion that DHTs cannot
cope with high churn rates [330]. By implementing methods for
continuous detection and repair, their MSPastry implementation
achieved shorter routing paths and a maintenance overhead of less
than half a message per second per node.
There have been more recent proposals based on these early Plaxton-
like schemes. Kademlia uses a bit-wise exclusive or (XOR) metric for
the 'distance' between 160-bit node identifiers [45]. Each node
keeps a list of contact nodes for each section of the node space that
is between 2^i and 2^(i+1) from itself (0.i<160). Longer-lived nodes
are deliberately given preference on this list -- it has been found
in Gnutella that the longer a node has been active, the more likely
it is to remain active. Like Kademlia, Willow uses the XOR metric
[32]. It implements a Tree Maintenance Protocol to 'zipper' together
broken segments of a tree. Where other schemes use DHT routing to
inefficiently add new peers, Willow can merge disjoint or broken
trees in O(log n) parallel operations.
3.5.2. Rings (Chord, DKS)
Chord is the prototypical DHT ring, so we first sketch its operation.
Chord maps nodes and keys to an identifier ring [7, 34]. Chord
supports one main operation: find a node with the given key. It uses
Consistent Hashing (Section 3.1) to minimize disruption of keys when
nodes join and leave the network. However, Chord peers need only
track O(log n) other peers, not all peers as in the original
consistent hashing proposal [49]. It enables concurrent node
insertions and deletions, improving on PRR. Compared to Pastry, it
has a simpler join protocol. Each Chord peer tracks its predecessor,
a list of successors, and a finger table. Using the finger table,
each hop is at least half the remaining distance around the ring to
the target node, giving an average lookup hop count of (1/2)log
n(base 2). Each Chord node runs a periodic stabilization routine
that updates predecessor and successor pointers to cater to newly
added nodes. All successors of a given node need to fail for the
ring to fail. Although a node departure could be treated the same as
a failure, a departing Chord node first notifies the predecessor and
successors, so as to improve performance.
In their definitive paper, Chord's inventors critiqued its
dependability under churn [34]. They provided proofs on the
behaviour of the Chord network when nodes in a stable network fail,
stressing that such proofs are inadequate in the general case of a
perpetually churning network. An earlier paper had posed the
question, "For lookups to be successful during churn, how regularly
do the Chord stabilization routines need to run?" [331]. Stoica,
Morris, et al. modeled a range of node join/departure rates and
stabilization periods for a Chord network of 1000 nodes. They
measured the number of timeouts (caused by a finger pointing to a
departed node) and lookup failures (caused by nodes that temporarily
point to the wrong successor during churn). They also modeled the
'lookup stretch', the ratio of the Chord lookup time to optimal
lookup time on the underlying network. They demonstrated the latency
advantage of recursive lookups over iterative lookups, but there
remains room for delay reduction. For further work, the authors
proposed to improve resilience to network partitions, using a small
set of known nodes or 'remembered' random nodes. To reduce the
number of messages per lookup, they suggested an increase in the size
of each step around the ring, accomplished via a larger number of
fingers at each node. Much of the paper assumed independent, equally
likely node failures. Analysis of correlated node failures, caused
by massive site or backbone failures, will be more important in some
deployments. The paper did not attempt to recommend a fixed optimal
stabilization rate. Liben-Nowell, Balakrishnan, et al. had suggested
that optimum stabilization rate might evolve according to
measurements of peers' behaviour [331] -- such a mechanism has yet to
be devised.
Alima, El-Ansary, et al. considered the communication costs of
Chord's stabilization routines, referred to as 'active correction',
to be excessive [332]. Two other robustness issues also motivated
their Distributed K-ary Search (DKS) design, which is similar to
Chord. Firstly, the total system should evolve for an optimum
balance between the number of peers, the lookup hop count, and the
size of the routing table. Secondly, lookups should be reliable --
P2P algorithms should be able to guarantee a successful lookup for
key/value pairs that have been inserted into the system. A similar
lookup-correctness issue was raised elsewhere by one of Chord's
authors; "Is it possible to augment the data structure to work even
when nodes (and their associated finger lists) just disappear?" [333]
Alima, El-Ansary, et al. asserted that P2Ps using active correction,
like Chord, Pastry, and Tapestry, are unable to give such a
guarantee. They propose an alternate 'correction-on-use' scheme,
whereby expired routing entries are corrected by information
piggybacking lookups and insertions. A prerequisite is that lookup
and insertion rates are significantly higher than node arrival,
departure, and failure rates. Correct lookups are guaranteed in the
presence of simultaneous node arrivals or up to f concurrent node
departures, where f is configurable.
3.5.3. Tori (CAN)
Ratnasamy, Francis, et al. developed the Content-Addressable Network
(CAN), another early DHT widely referenced alongside Tapestry,
Pastry, and Chord [8, 334]. It is arranged as a virtual
d-dimensional Cartesian coordinate space on a d-torus. Each node is
responsible for a zone in this coordinate space. The designers used
a heuristic thought to be important for large, churning P2P networks:
keep the number of neighbours independent of system size.
Consequently, its design differs significantly from Pastry, Tapestry,
and Chord. Whereas they have O(log n) neighbours per node and O(log
n) hops per lookup, CAN has O(d) neighbours and O(dn^(1/d)) hop
count. When CAN's system-wide parameter d is set to log(n), CAN
converges to their profile. If the number of nodes grows, a major
rearrangement of the CAN network may be required [151]. The CAN
designers considered building on PRR, but opted for the simple, low-
state-per-node CAN algorithm instead. They had reasoned that a PRR-
based design would not perform well under churn, given node
departures and arrivals would affect a logarithmic number of nodes
[8].
There have been preliminary assessments of CAN's resilience. When a
node leaves the CAN in an orderly fashion, it passes its own Virtual
ID (VID), its neighbours' VIDs and IP addresses, and its key/value
pairs to a takeover node. If a node leaves abruptly, its neighbours
send recovery messages towards the designated takeover node. CAN
ensures the recovery messages reach the takeover node, even if nodes
die simultaneously, by maintaining a VID chain with Chord's
stabilization algorithm. Some initial 'proof of concept' resilience
simulations were run using the Network Simulator (NS) [335] for up to
a few hundred nodes. Average hop counts and lookup failure
probabilities were plotted against the total number of nodes for
various node failure rates [8]. The CAN team documented several open
research questions pertaining to state/hop count trade-offs,
resilience, load, locality, and heterogeneous peers [44, 334].
3.5.4. Butterflies (Viceroy)
Viceroy approximates a butterfly network [46]. It generally has
constant degree like CAN. Like Chord, Tapestry, and Pastry, it has
logarithmic diameter. It improves on these systems, inasmuch as its
diameter is better than CAN and its degree is better than Chord,
Tapestry, and Pastry. As with most DHTs, it utilizes Consistent
Hashing. When a peer joins the Viceroy network, it takes a random
but permanent 'identity' and selects its 'level' within the network.
Each peer maintains general ring pointers ('predecessor' and
'successor'), level ring pointers ('nextonlevel' and 'prevonlevel'),
and butterfly pointers ('left', 'right', and 'up'). When a peer
departs, it normally passes its key pairs to a successor, and
notifies other peers to find a replacement peer.
The Viceroy paper scoped out the issue of robustness. It explicitly
assumed that peers do not fail [46]. It assumed that join and leave
operations do not overlap, so as to avoid the complication of
concurrency mechanisms like locking. Kaashoek and Karger were
somewhat critical of Viceroy's complexity [37]. They also pointed to
its fault-tolerance blind spot. Li and Plaxton suggested that such
constant-degree algorithms deserve further consideration [47]. They
offered several pros and cons. The limited degree may increase the
risk of a network partition, or inhibit use of local neighbours (for
the simple reason that there are less of them). On the other hand,
it may be easier to reason about the correctness of fixed-degree
networks. One of the Viceroy authors has since proposed constant-
degree peers in a two-tier, locality-aware DHT [310] -- the lower
degree maintained by each lower-tier peer purportedly improves
network adaptability. Another Viceroy author has since explored an
alternative bounded-degree graph for P2P, namely the de Bruijn graph
[336].
3.5.5. de Bruijn (D2B, Koorde, Distance Halving, ODRI)
De Bruijn graphs have had numerous refinements since their inception
[337, 338]. Schlumberger was the first to use them for networking
[339]. Two research teams independently devised the 'generalized' de
Bruijn graph that accommodates a flexible number of nodes in the
system [340, 341]. Rowley and Bose studied fault-tolerant rings
overlaid on the de Bruijn graph [342]. Lee, Liu, et al. devised a
two-level de Bruijn hierarchy, whereby clusters of local nodes are
interconnected by a second-tier ring [343].
Many of the algorithms discussed previously are 'greedy' in that each
time a query is forwarded, it moves closer to the destination.
Unfortunately, greedy algorithms are generally suboptimal -- for a
given degree, the routing distance is longer than necessary [344].
Unlike these earlier P2P designs, de Bruijn graphs of degree k
achieve an asymptotically optimal diameter log n, where n is the
number of nodes in the system and k can be varied to improve
resilience. If there are O(log n) neighbours per node, the de Bruijn
hop count is O(log n/log log n). To illustrate de Bruijn's practical
advantage, consider a network with one million nodes of degree 20:
Chord has a diameter of 20, while de Bruijn has a diameter of 5 [36].
In 2003, there were a quick succession of de Bruijn proposals -- D2B
[345], Koorde [37], Distance Halving [132, 336], and the Optimal
Diameter Routing Infrastructure (ODRI) [36].
Fraigniaud and Gauron began the D2B design by laying out an informal
problem statement: keys should be evenly distributed; lookup latency
should be small; traffic load should be evenly distributed; updates
of routing tables and redistribution of keys should be fast when
nodes join or leave the network. They defined a node's "congestion"
to be the probability that a lookup will traverse it. Apart from its
optimal de Bruijn diameter, they highlighted D2B's merits: a constant
expected update time when nodes join and leave (O(log n) with high
probability (w.h.p.)); the expected node congestion is O((log n)/n)
(O(((log n)^2)/n) w.h.p.) [345]. D2B's resilience was discussed only
in passing.
Koorde extends Chord to attain the optimal de Bruijn degree/diameter
trade-off above [37]. Unlike D2B, Koorde does not constrain the
selection of node identifiers. Also unlike D2B, it caters to
concurrent joins, by extension of Chord's functionality. Kaashoek
and Karger investigated Koorde's resilience to a rather harsh failure
scenario: "in order for a network to stay connected when all nodes
fail with probability of 1/2, some nodes must have degree
omega(log n)" [37]. They sketched a mechanism to increase Koorde's
degree for this more stringent fault tolerance, losing de Bruijn's
constant degree advantage. Similarly, to achieve a constant-factor
load balance, Koorde would have to sacrifice its degree optimality.
They suggested that the ability to trade the degree, and hence the
maintenance overhead, against the expected hop count may be important
for churning systems. They also identified an open problem: find a
load-balanced, degree optimal DHT. Datta, Girdzijauskas, et al.
showed that for arbitrary key distributions, de Bruijn graphs fail to
meet the dual goals of load balancing and search efficiency [346].
They posed the question, "(Is there) a constant routing table sized
DHT which meets the conflicting goals of storage load balancing and
search efficiency for an arbitrary and changing key distribution?"
Distance Halving was also inspired by de Bruijn [336] and shares its
optimal diameter. Naor and Wieder argued for a two-step
"continuous-discrete" approach for its design. The correctness of
its algorithms is proven in a continuous setting. The algorithms are
then mapped to a discrete space. The source x and target y are
points on the continuous interval [0,1). Data items are hashed to
this same interval. <str> is a string that determines how messages
leave any point on the ring: if bit t of the string is 0, the left
leg is taken; if it is 1, the right leg is taken. <str> increases by
one bit each hop, giving a sequence by which to step around the ring.
A lookup has two phases. In the first, the lookup message containing
the source, target, and the random string hops toward the midpoint of
the source and target. On each hop, the distance between <str>(x)
and <str>(y) is halved, by virtue of the specific 'left' and 'right'
functions. In the second phase, the message steps 'backward' from
the midpoint to the target, removing the last bit in <str> at each
hop. 'Join' and 'leave' algorithms were outlined but there was no
consideration of recovery times or message load on churn. Using the
Distance Halving properties, the authors devised a caching scheme to
relieve congestion in a large P2P network. They have also modified
the algorithm to be more robust in the presence of random faults
[132].
Solid comparisons of DHT resilience are scarce, but Loguinov, Kumar,
et al. give just that in their ODRI paper [36]. They compare Chord,
CAN, and de Bruijn in terms of routing performance, graph expansion
and clustering. At the outset, they give the optimal diameter (the
maximum hop count between any two nodes in the graph) and average hop
count for graphs of fixed degree. De Bruijn graphs converge to both
optima, and outperform Chord and CAN on both counts. These optima
impact both delay and aggregate lookup load. They present two
clustering measures (edge expansion and node expansion), which are
interesting for resilience. Unfortunately, after decades of de
Bruijn research, they have no exact solution. De Bruijn was shown to
be superior in terms of path overlap - "de Bruijn automatically
selects backup paths that do not overlap with the best shortest path
or with each other" [36].
3.5.6. Skip Graphs
Skip Graphs have been pursued by two research camps [38, 41]. They
augment the earlier Skip Lists [347, 348]. Unlike earlier balanced
trees, the Skip List is probabilistic -- its insert and delete
operations do not require tree rearrangements and so are faster by a
constant factor. The Skip List consists of layers of ordered linked
lists. All nodes participate in the bottom layer 0 list. Some of
these nodes participate in the layer 1 list with some fixed
probability. A subset of layer 1 nodes participate in the layer 2
list, and so on. A lookup can proceed quickly through the list by
traversing the sparse upper layers until it is close to, or at, the
target. Unfortunately, nodes in the upper layers of a Skip List are
potential hot spots and single points of failure. Unlike Skip Lists,
Skip Graphs provide multiple lists at each level for redundancy, and
every node participates in one of the lists at each level.
Each node in a Skip Graph has theta(log n) neighbours on average,
like some of the preceding DHTs. The Skip Graph's primary edge over
the DHTs is its support for prefix and proximity search. DHTs hash
objects to a random point in the graph. Consequently, they give no
guarantees over where the data is stored. Nor do they guarantee that
the path to the data will stay within the one administration as far
as possible [38]. Skip graphs, on the other hand, provide for
location-sensitive name searches. For example, to find the document
docname on the node user.company.com, the Skip Graph might step
through its ordered lists for the prefix com.company.user [38].
Alternatively, to find an object with a numeric identifier, an
algorithm might search the lowest layer of the Skip Graph for the
first digit, the next layer for the next digit, in the same vein
until all digits are resolved. Being ordered, Skip Graphs also
facilitate range searches. In each of these examples, the Skip Graph
can be arranged such that the path to the target, as far as possible,
stays within an administrative boundary. If one administration is
detached from the rest of the Skip Graph, routing can continue within
each of the partitions. Mechanisms have been devised to merge
disconnected segments [157], though at this stage, segments are re-
merged one at a time. A parallel merge algorithm has been flagged
for future work.
The advantages of Skip Graphs come at a cost. To be able to provide
range queries and data placement flexibility, Skip Graph nodes
require many more pointers than their DHT counterparts. An increased
number of pointers implies increased maintenance traffic. Another
shortcoming of at least one of the early proposals was that no
algorithm was given to assign keys to machines. Consequently, there
are no guarantees on system-wide load balancing or on the distance
between adjacent keys [100]. Aspnes, Kirsch, et al. have recently
devised a scheme to reduce the inter-machine pointer count from
O(mlogm), where m is the number of data elements, to O(nlog n), where
n is the number of nodes [100]. They proposed a two-layer scheme --
one layer for the Skip Graph itself and the second 'bucket layer'.
Each machine is responsible for a number of buckets and each bucket
elects a representative key. Nodes locally adjust their load. They
accept additional keys if they are below their threshold or disperse
keys to nearby nodes if they are above threshold. There appear to be
numerous open issues: simulations have been done but analysis is
outstanding; mechanisms are required to handle the arrival and
departure of nodes; there were only brief hints as to how to handle
nodes with different capacities.
4. Semantic Index
Semantic indexes capture object relationships. While the semantic-
free methods (DHTs) have firmer theoretic foundations and guarantee
that a key can be found if it exists, they do not capture the
relationships between the document name and its content or metadata
on their own. Semantic P2P designs do. However, since their design
is often driven by heuristics, they may not guarantee that scarce
items will be found.
So what might the semantically indexed P2Ps add to an already crowded
field of distributed information architectures? At one extreme,
there are the distributed relational database management systems
(RDBMSs), with their strong consistency guarantees [284]. They
provide strong data independence, the flexibility of SQL queries, and
strong transactional semantics -- Atomicity, Consistency, Isolation
and Durability (ACID) [349]. They guarantee that the query response
is complete -- all matching results are returned. The price is
performance. They scale to perhaps 1000 nodes, as evidenced in
Mariposa [350, 351], or require query caching front ends to constrain
the load [284]. Database research has "arguably been cornered into
traditional, high-end, transactional applications" [72]. Then there
are distributed file systems, like the Network File System (NFS) or
the Serverless Network File Systems (xFS), with little data
independence, low-level file retrieval interfaces, and varied
consistency [284]. Today's eclectic mix of Content Distribution
Networks (CDNs) generally deload primary servers by redirecting Web
requests to a nearby replica. Some intercept the HTTP requests at
the DNS level and then use consistent hashing to find a replica [23].
Since this same consistent hashing was a forerunner to the DHT
approaches above, CDNs are generally constrained to the same simple
key lookups.
The opportunity for semantically indexed P2Ps, then, is to provide:
a) graduated data independence, consistency, and query flexibility,
and
b) probabilistically complete query responses, across
c) very large numbers of low-cost, geographically distributed,
dynamic nodes.
4.1. Keyword Lookup
P2P keyword lookup is best understood by considering the structure of
the underlying index and the algorithms by which queries are routed
over that index. Figure 3 summarizes the following paragraphs by
classifying the keyword query algorithms, index structures, and
metrics. The research has largely focused on scalability, not
dependability. There have been very few studies that quantify the
impact of network churn. One exception is the work by Chawathe, et
al. on the Gia system [61]. Gia's combination of algorithms from
Figure 3 (receiver-based flow control, biased random walk, and one-
hop replication) gave 2-4 orders of magnitude improvement in query
success rates in churning networks.
QUERY
Query routing
Flooding: Peers only index local files so queries must propagate
widely [4]
Policy-based: Choice of the next hop node: random; most/least
recently used; most files shared; most results [265, 352]
Random walks: Parallel [67] or biased random walks [61, 66]
Query forwarding
Iterative: Nodes perform iterative unicast searches of ultrapeers,
until the desired number of results is achieved. See Gnutella
UDP Extension for Scalable Searches (GUESS) [265, 353]
Recursive
Query flow control
Receiver-controlled: Receivers grant query tokens to senders, so
as to avoid overload [61]
Reactive: sender throttles queries when it notices receivers are
discarding packets [61, 66]
Dynamic Time To Live: In the Dynamic Query Protocol, the sender
adjusts the time-to-live on each iteration based on the number
of results received, the number of connections left, and the
number of nodes already theoretically reached by the search [354]
INDEX
Distribution
Compression: Leaf nodes periodically send ultrapeers compressed
query routing tables, as in the Query Routing Protocol [260]
One hop replication: Nodes maintain an index of content on their
nearest neighbors [61, 352]
Partitioning
By document [210]
By keyword: Use an inverted list to find a matching document,
either locally or at another peer [21]. Partition by keyword
sets [355]
By document and keyword: Also called Multi-Level Partitioning [21]
METRIC
Query load: Queries per second per node/link [65, 265]
Degree: The number of links per node [66, 352]. Early P2P networks
approximated power-law networks, where the number of nodes with L
links is proportional to L^(-k), where k is a constant [65]
Query delay: Reported in terms of time and hop count [61, 66]
Query success rate: The "Collapse Point" is the per-node query rate
at which the query success rate drops below 90% [61]. See
also [61, 265, 352].
Figure 3: Keyword Lookup in P2P Systems
4.1.1. Gnutella Enhancements
Perhaps the most widely referenced P2P system for simple keyword
match is Gnutella [4]. Gnutella queries contain a string of
keywords. Gnutella peers answer when they have files whose names
contain all the keywords. As discussed in Section 2.1, early
versions of Gnutella did not forward the document index. Queries
were flooded and peers searched their own local indexes for filename
matches. An early review highlighted numerous areas for improvement
[65]. It was estimated that the query traffic alone from 50,000
early-generation Gnutella nodes would amount to 1.7% of the total
U.S. Internet backbone traffic at December 2000 levels. It was
speculated that high-degree Gnutella nodes would impede
dependability. An unnecessarily high percentage of Gnutella traffic
crossed Autonomous System (AS) boundaries -- a locality mechanism may
have found suitable nearby peers.
Fortunately, there have since been numerous enhancements within the
Gnutella Developer Forum. At the time of writing, it has been
reported that Gnutella has almost 350,000 unique hosts, of which
nearly 90,000 accept incoming connections [356]. One of the main
improvements is that an index of filename keywords, called the Query
Routing Table (QRT), can now be forwarded from 'leaf peers' to its
'ultrapeers' [260]. Ultrapeers can then ensure that the leaves only
receive queries for which they have a match, dramatically reducing
the query traffic at the leaves. Ultrapeers can have connections to
many leaf nodes (~10-100) and a small number of other ultrapeers
(<10) [260]. Originally, a leaf node's QRT was not forwarded by the
parent ultrapeer to other ultrapeers. More recently, there has been
a proposal to distribute aggregated QRTs amongst ultrapeers [357].
To further limit traffic, QRTs are compressed by hashing, according
to the Query Routing Protocol (QRP) specification [281]. This same
specification claims QRP may reduce Gnutella traffic by orders of
magnitude, but cautions that simulation is required before mass
deployment. A known shortcoming of QRP was that the extent of query
propagation was independent of the popularity of the search terms.
The Dynamic Query Protocol addressed this [358]. It required leaf
nodes to send single queries to high-degree ultrapeers that adjust
the queries' time-to-live (TTL) bounds according to the number of
received query results. An earlier proposal, called the Gnutella UDP
Extension for Scalable Searches (GUESS) [353], similarly aimed to
reduce the number of queries for widely distributed files. GUESS
reuses the non-forwarding idea (Section 2). A GUESS peer repeatedly
queries single ultrapeers with a TTL of 1, with a small timeout on
each query to limit load. It chooses the number of iterations and
selects ultrapeers so as to satisfy its search needs. For
adaptability, a small number of experimental Gnutella nodes have
implemented eXtensible Markup Language (XML) schemas for richer
queries [359, 360]. None of the above Gnutella proposals explicitly
assess robustness.
The broader research community has recently been leveraging aspects
of the Gnutella design. Lv, Ratnasamy, et al. exposed one assumption
implicit in some of the early DHT work -- that designs "such as
Gnutella are inherently not scalable, and therefore should be
abandoned" [66]. They argued that by making better use of the more
powerful peers, Gnutella's scalability issues could be alleviated.
Instead of its flooding mechanism, they used random walks. Their
preliminary design to bias random walks towards high capacity nodes
did not go as far as the ultrapeer proposals in that the indexes did
not move to the high-capacity nodes. Chawathe, Ratnasamy, et al.
chose to extend the Gnutella design with their Gia system, in
response to the perceived shortcomings of DHTs in Section 1.2 [61].
Compared to the early Gnutella designs, they incorporated several
novel features. They devise a topology adaptation algorithm so that
most peers are attached to high-degree peers. They use a random walk
search algorithm, in lieu of flooding, and bias the query load
towards higher-degree peers. For 'one-hop replication', they require
all nodes to keep pointers to content on adjacent peers. To
implement a receiver-controlled token-based flow control, a peer must
have a token from its neighbouring peer before it sends a query to
it. Chawathe, Ratnasamy, et al. show by simulations that the
combination of these features provides a scalability improvement of
three to five orders of magnitude over Gnutella "while retaining
significant robustness". The main robustness metrics they used were
the 'collapse point' query rate (the per-node query rate at which the
successful query rate falls below 90%) and the average hop count
immediately prior to collapse. Their comparison with Gnutella did
not take into account the Gnutella enhancements above -- this was
left as future work. Castro, Costa, and Rowstron argued that if
Gnutella were built on top of a structured overlay, then both the
query and overlay maintenance traffic could be reduced [259]. Yang,
Vinograd, et al. explore various policies for peer selection in the
GUESS protocol, since the issue is left open in the original proposal
[265]. For example, the peer initiating the query could choose peers
that have been "most recently used" or that have the "most files
shared". Various policy pitfalls are identified. For example, good
peers could be overloaded, victims of their own success.
Alternatively, malicious peers could encourage the querying peer to
try inactive peers. They conclude that a "most results" policy gives
the best balance of robustness and efficiency. Like Castro, Costa,
and Rowstron, they concentrated on the static network scenario.
Cholvi, Felber, et al. very briefly describe how similar "least
recently used" and "most often used" heuristics can be used by a peer
to select peer 'acquaintances' [352]. They were motivated by the
congestion associated with Gnutella's TTL-limited flooding.
Recognizing that the busiest peers can quickly become overloaded
central hubs for the entire network, they limit the number of
acquaintances for any given peer to 25. They sketch a mechanism to
decrement a query's TTL multiple times when it traverses "interested
peers". In summary, these Gnutella-related investigations are
characterized by a bias for high-degree peers and very short directed
query paths, a disdain for flooding, and concern about excessive load
on the 'better' peers. Generally, the robustness analysis for
dynamic networks (content updates and node arrivals/departures)
remains open.
4.1.2. Partition-by-Document, Partition-by-Keyword
One aspect of P2P keyword search systems has received particular
attention: should the index be partitioned by document or by keyword?
The issue affects scalability. To be partitioned by document, each
node has a local index of documents for which it is responsible.
Gnutella is a prime example. Queries are generally flooded in
systems partitioned by document. On the other hand, a peer may
assume responsibility for a set of keywords. The peer uses an
inverted list to find a matching document, either locally or at
another peer. If the query contains several keywords, inverted lists
may need to be retrieved from several different peers to find the
intersection [21]. The initial assessment by Li, Loo, et al. was
that the partition-by-document approach was superior [210]. For one
scenario of a full-text Web search, they estimated the communications
costs to be about six times higher than the feasible budget.
However, wanting to exploit prior work on inverted list intersection,
they studied the partition-by-keyword strategy. They proposed
several optimizations that put the communication costs for a
partition-by-keyword system within an order of magnitude of
feasibility. There had been a couple of prior papers that suggested
partitioned-by-keyword designs incorporate DHTs to map keywords to
peers [355, 361]. In Gnawali's Keyword-set Search System (KSS), the
index is partitioned by sets of keywords [355]. Terpstra, Behnel, et
al. point out that by keeping keyword pairs or triples, the number of
lists per document in KSS is squared or tripled [362]. Shi,
Guangwen, et al. interpreted the approximations of Li, Loo, et al. to
mean that neither approach is feasible on its own [21]. Their
Multi-Level Partitioning (MLP) scheme incorporates both partitioning
approaches. They arrange nodes into a group hierarchy, with all
nodes in the single 'level 0' group, and with the same nodes sub-
divided into k logical subgroups on 'level 1'. The subgroups are
again divided, level by level, until level l. The inverted index is
partitioned by document between groups and by keyword within groups.
MLP avoids the query flooding normally associated with systems
partitioned by document, since a small number of nodes in each group
process the query. It reduces the bandwidth overheads associated
with inverted list intersection in systems partitioned solely by
keyword, since groups can calculate the intersection independently
over the documents for which they are responsible. MLP was overlaid
on SkipNet, per Section 3.5.6 [38]. Some initial analyses of
communications costs and query latencies were provided.
4.1.3. Partial Search, Exhaustive Search
Much of the research above addresses partial keyword search.
Daswani, et al. highlighted the open problem of efficient,
comprehensive keyword search [25]. How can exhaustive searches be
achieved without flooding queries to every peer in the network?
Terpstra, Behnel et al. couched the keyword search problem in
rendezvous terms: dynamic keyword queries need to 'meet' with static
document lists [362]. Their Bitzipper scheme is partitioned by
document. They improved on full flooding by putting document
metadata on 2sqrt(n) nodes and forwarding queries through only
6sqrt(n) nodes. They reported that Bitzipper nodes need only 1/166th
of the bandwidth of full-flooding Gnutella nodes for an exhaustive
search. An initial comparison of query load was given. There was
little consideration of either static or dynamic resilience; that is,
of nodes failing, of documents continually changing, or of nodes
continually joining and leaving the network.
4.2. Information Retrieval
The field of Information Retrieval (IR) has matured considerably
since its inception in the 1950s [363]. A taxonomy for IR models has
been formalized [262]. It consists of four elements: a
representation of documents in a collection; a representation of user
queries; a framework describing relationships between document
representations and queries; and a ranking function that quantifies
an ordering amongst documents for a particular query. Three main
issues motivate current IR research -- information relevance, query
response time, and user interaction with IR systems. The dominant IR
trends for searching large text collections are also threefold [262].
The size of collections is increasing dramatically. More complicated
search mechanisms are being found to exploit document structure, to
accommodate heterogeneous document collections, and to deal with
document errors. Compression is in favour -- it may be quicker to
search compact text or retrieve it from external devices. In a
distributed IR system, query processing has four parts. Firstly,
particular collections are targeted for the search. Secondly,
queries are sent to the targeted collections. Queries are then
evaluated at the individual collections. Finally, results from the
collections are collated.
So how do P2P networks differ from distributed IR systems? Bawa,
Manku, et al. presented four differences [62]. They suggested that a
P2P network is typically larger, with tens or hundreds of thousands
of nodes. It is usually more dynamic, with node lifetimes measured
in hours. They suggested that a P2P network is usually homogeneous,
with a common resource description language. It lacks the
centralized "mediators" found in many IR systems that assume
responsibility for selecting collections, for rewriting queries, and
for merging ranked results. These distinctions are generally aligned
with the peer characteristics in Section 1. One might add that P2P
nodes display more symmetry -- peers are often both information
consumers and producers. Daswani, Garcia-Molina, et al. pointed out
that, while there are IR techniques for ranked keyword search at
moderate scale, research is required so that ranking mechanisms are
efficient at the larger scale targeted by P2P designs [25]. Joseph
and Hoshiai surveyed several P2P systems using metadata techniques
from the IR toolkit [60]. They described an assortment of IR
techniques and P2P systems, including various metadata formats,
retrieval models, bloom filters, DHTs, and trust issues.
In the ensuing paragraphs, we survey P2P work that has incorporated
information retrieval models, particularly the Vector Model and the
Latent Semantic Indexing Model. We omit the P2P work based on
Bayesian models. Some have pointed to such work [60], but made no
explicit mention of the model [364]. One early paper on P2P
content-based image retrieval also leveraged the Bayesian model
[365]. For the former two models, we briefly describe the design,
then try to highlight robustness aspects. On robustness, we are
again stymied for lack of prior work. Indeed, a search across all
proceedings of the Annual ACM Conference on Research and Development
in Information Retrieval for the words "reliable", "available",
"dependable", or "adaptable" did not return any results at the time
of writing. In contrast, a standard text on distributed database
management systems [366] contains a whole chapter on reliability. IR
research concentrates on performance measures. Common performance
measures include recall, the fraction of the relevant documents that
has been retrieved and precision, the fraction of the retrieved
documents that is relevant [262]. Ideally, an IR system would have
high recall and high precision. Unfortunately techniques favouring
one often disadvantage the other [363].
4.2.1. Vector Model (PlanetP, FASD, eSearch)
The vector model [367] represents both documents and queries as term
vectors, where a term could be a word or a phrase. If a document or
query has a term, the weight of the corresponding dimension of the
vector is non-zero. The similarity of the document and query vectors
gives an indication of how well a document matches a particular
query.
The weighting calculation is critical across the retrieval models.
Amongst the numerous proposals for the probabilistic and vector
models, there are some commonly recurring weighting factors [363].
One is term frequency. The more a term is repeated in a document,
the more important the term is. Another is inverse document
frequency. Terms common to many documents give less information
about the content of a document. Then there is document length.
Larger documents can bias term frequencies, so weightings are
sometimes normalized against document length. The expression "TFIDF
weighting" refers to the collection of weighting calculations that
incorporate term frequency and inverse document frequency, not just
to one. Two weighting calculations have been particularly dominant
-- Okapi [368] and pivoted normalization [369]. A distributed
version of Google's Pagerank algorithm has also been devised for a
P2P environment [370]. It allows incremental, ongoing Pagerank
calculations while documents are inserted and deleted.
A couple of early P2P systems leveraged the vector model. Building
on the vector model, PlanetP divided the ranking problem into two
steps [215]. In the first, peers are ranked for the probability that
they have matching documents. In the second, higher-priority peers
are contacted and the matching documents are ranked. An Inverse Peer
Frequency, analogous to the Inverse Document Frequency, is used to
rank relevant peers. To further constrain the query traffic, PlanetP
contacts only the first group of m peers to retrieve a relevant set
of documents. In this way, it repeatedly contacts groups of m peers
until the top k document rankings are stable. While the PlanetP
designers first quantified recall and precision, they also considered
reliability. Each PlanetP peer has a global index with a list of all
other peers, their IP addresses, and their Bloom filters. This large
volume of shared information needs to be maintained. Klampanos and
Jose saw this as PlanetP's primary shortcoming [371]. Each Bloom
filter summarized the set of terms in the local index of each peer.
The time to propagate changes, be they new documents or peer
arrivals/departures, was studied by simulation for up to 1000 peers.
The reported propagation times were in the hundreds of seconds.
Design workarounds were required for PlanetP to be viable across
slower dial-up modem connections. For future work, the authors were
considering some sort of hierarchy to scale to larger numbers of
peers.
A second early system using the vector model is the Fault-tolerant,
Adaptive, Scalable Distributed (FASD) search engine [283], which
extended the Freenet design (Section 2.3) for richer queries. The
original Freenet design could find a document based on a globally
unique identifier. Kronfol's design added the ability to search, for
example, for documents about "apples AND oranges NOT bananas". It
uses a TFIDF weighting scheme to build a document's term vector.
Each peer calculates the similarity of the query vector and local
documents and forwards the query to the best downstream peer. Once
the best downstream peer returns a result, the second-best peer is
tried, and so on. Simulations with 1000 nodes gave an indication of
the query path lengths in various situations -- when routing queries
in a network with constant rates of node and document insertion, when
bootstrapping the network in a "worst-case" ring topology, or when
failing randomly and specifically selected peers. Kronfol claimed
excellent average-case performance -- less than 20 hops to retrieve
the same top n results as a centralized search engine. There were,
however, numerous cases where the worst-case path length was several
hundred hops in a network of only 1000 nodes.
In parallel, there have been some P2P designs based on the vector
model from the University of Rochester -- pSearch [9, 372] and
eSearch [373]. The early pSearch paper suggested a couple of
retrieval models, one of which was the Vector Space Model, to search
only the nodes likely to have matching documents. To obtain
approximate global statistics for the TFIDF calculation, a spanning
tree was constructed across a subset of the peers. For the m top
terms, the term-to-document index was inserted into a Content-
Addressable Network [334]. A variant that mapped terms to document
clusters was also suggested. eSearch is a hybrid of the partition-
by-document and partition-by-term approaches (Section 4.1.2) eSearch
nodes are primarily partitioned by term. Each is responsible for the
inverted lists for some top terms. For each document in the inverted
list, the node stores the complete term list. To reduce the size of
the index, the complete term lists for a document are only kept on
nodes that are responsible for top terms in the document. eSearch
uses the Okapi term weighting to select top terms. It relies on the
Chord DHT [34] to associate terms with nodes storing the inverted
lists. It also uses automatic query expansion. This takes the
significant terms from the top document matches and automatically
adds them to the user's query to find additional relevant documents.
The eSearch performance was quantified in terms of search precision,
the number of retrieved documents, and various load-balancing
metrics. Compared to the more common proposals for partitioning by
keywords, eSearch consumed 6.8 times the storage space to achieve
faster search times.
4.2.2. Latent Semantic Indexing (pSearch)
Another retrieval model used in P2P proposals is Latent Semantic
Indexing (LSI) [374]. Its key idea is to map both the document and
query vectors to a concept space with lower dimensions. The starting
point is a t*N weighting matrix, where t is the total number of
indexed terms, N is the total number of documents, and the matrix
elements could be TFIDF rankings. Using singular value
decomposition, this matrix is reduced to a smaller number of
dimensions, while retaining the more significant term-to-document
mappings. Baeza-Yates and Ribeiro-Neto suggested that LSI's value is
a novel theoretic framework, but that its practical performance
advantage for real document collections had yet to be proven [262].
pSearch incorporated LSI [9]. By placing the indices for
semantically similar documents close in the network, Tang, Xu, et al.
touted significant bandwidth savings relative to the early full-
flooding variant of Gnutella [372]. They plotted the number of nodes
visited by a query. They also explored the trade-off with accuracy,
the percentage match between the documents returned by the
distributed pSearch algorithm and those from a centralized LSI
baseline. In a more recent update to the pSearch work, Tang,
Dwarkadas, et al. summarized LSI's shortcomings [375]. Firstly, for
large document collections, its retrieval quality is inherently
inferior to Okapi. Secondly, singular value decomposition consumes
excessive memory and computation time. Consequently, the authors
used Okapi for searching while retaining LSI for indexing. With
Okapi, they selected the next node to be searched and selected
documents on searched nodes. With LSI, they ensured that similar
documents are clustered near each other, thereby optimizing the
network search costs. When retrieving a small number of top
documents, the precision of LSI+Okapi approached that of Okapi.
However, if retrieving a large number of documents, the LSI+Okapi
precision is inferior. The authors want to improve this in future
work.
4.2.3. Small Worlds
The "small world" concept originally described how people are
interconnected by short chains of acquaintances [376]. Kleinberg was
struck by the algorithmic lesson of the small world, namely "that
individuals using local information are collectively very effective
at constructing short paths between two points in a social network"
[377]. Small world networks have a small diameter and a large
clustering coefficient (a large number of connections amongst
relevant nodes) [378].
The small world idea has had a limited impact on peer-to-peer
algorithms. It has influenced only a few unstructured [62, 378-380]
and structured [344, 381] algorithms. The most promising work on
"small worlds" in P2P networks are those concerned with the
information retrieval metrics, precision and recall [62, 378, 380].
5. Queries
Database research suggests directions for P2P research. Hellerstein
observed that, while work on fast P2P indexes is well underway, P2P
query optimization remains a promising topic for future research
[23]. Kossman reviewed the state of the art of distributed query
processing, highlighting areas for future research: simulation and
query optimization for networks of tens of thousands of servers and
millions of clients; non-relational data types (e.g., XML, text, and
images); and partial query responses since on the Internet, "failure
is the rule rather than the exception" [19]. A primary motivation
for the P2P system, PIER, was to scale from the largest database
systems of a few hundred nodes to an Internet environment in which
there are over 160 million nodes [22]. Litwin and Sahri have also
considered ways to combine distributed hashing, more specifically the
Scalable Distributed Data Structures, with SQL databases, claiming to
be first to implement scalable distributed database partitioning
[382]. Motivated by the lack of transparent distribution in current
distributed databases, they measure query execution times for
Microsoft SQL servers aggregated by means of an SDDS layer. One of
their starting assumptions was that it is too challenging to change
the SQL query optimizer.
Database research also suggests the approach to P2P research.
Researchers of database query optimization were divided between those
looking for optimal solutions in special cases and those using
heuristics to answer all queries [383]. Gribble, et al. cast query
optimization in terms of the data placement problem, which is to
"distribute data and work so the full query workload is answered with
lowest cost under the existing bandwidth and resource constraints"
[250]. They pointed out that even the static version of this problem
is NP-complete in P2P networks. Consequently, research on massive,
dynamic P2P networks will likely progress using both strategies of
early database research - heuristics and special-case optimizations.
If P2P networks are going to be adaptable, if they are to support a
wide range of applications, then they need to accommodate many query
types [72]. Up to this point, we have reviewed queries for keys
(Section 3) and keywords (Sections 4.1. and 4.2). Unfortunately, a
major shortcoming of the DHTs in Section 3.5 is that they primarily
support exact-match, single-key queries. Skip Graphs support range
and prefix queries, but not aggregation queries. Here we probe below
the language syntax to identify the open research issues associated
with more expressive P2P queries [25]. Triantafillou and Pitoura
observed the disparate P2P designs for different types of queries and
so outlined a unifying framework [76]. To classify queries, they
considered the number of relations (single or multiple), the number
of attributes (single or multiple), and the type of query operator.
They described numerous operators: equality, range, join, and
"special functions". The latter referred to aggregation (like sum,
count, average, minimum, and maximum), grouping and ordering. The
following sections approximately fit their taxonomy -- range queries,
multi-attribute queries, join queries and aggregation queries. There
has been some initial P2P work on other query types -- continuous
queries [20, 22, 73], recursive queries [22, 74], and adaptive
queries [23, 75]. For these, we defer to the primary references.
5.1. Range Queries
The support of efficient range predicates in P2P networks was
identified as an important open research issue by Huebsch, et al.
[22]. Range partitioning has been important in parallel databases to
improve performance, so that a transaction commonly needs data from
only one disk or node [22]. One type of range search, longest prefix
match, is important because of its prevalence in routing schemes for
voice and data networks alike. In other applications, users may pose
broad, inexact queries, even though they require only a small number
of responses. Consequently, techniques to locate similar ranges are
also important [77]. Various proposals for range searches over P2P
networks are summarized in Figure 4. Since the Scalable Distributed
Data Structure (SDDS) has been an important influence on contemporary
Distributed Hash Tables (DHTs) [49-51], we also include ongoing work
on SDDS range searches.
PEER-TO-PEER (P2P)
Locality Sensitive Hashing (Chord) [77]
Prefix Hash Trees (unspecified DHT) [78, 79]
Space Filling Curves (CAN) [80]
Space Filling Curves (Chord) [81]
Quadtrees (Chord) [82]
Skip Graphs [38, 41, 83, 100]
Mercury [84]
P-Grid [85, 86]
SCALABLE DISTRIBUTED DATA STRUCTURES (SDDS)
RP* [87, 88]
Figure 4: Solutions for Range Queries on P2P and SDDS Indexes
The papers on P2P range search can be divided into those that rely on
an underlying DHT (the first five entries in Figure 4) and those that
do not (the subsequent three entries). Bharambe, Agrawal, et al.
argued that DHTs are inherently ill-suited to range queries [84].
The very feature that makes for their good load balancing properties,
randomized hash functions, works against range queries. One possible
solution would be to hash ranges, but this can require a priori
partitioning. If the partitions are too large, partitions risk
overload. If they are too small, there may be too many hops.
Despite these potential shortcomings, there have been several range
query proposals based on DHTs. If hashing ranges to nodes, it is
entirely possible that overlapping ranges map to different nodes.
Gupta, Agrawal, et al. rely on locality sensitive hashing to ensure
that, with high probability, similar ranges are mapped to the same
node [77]. They propose one particular family of locality sensitive
hash functions, called min-wise independent permutations. The number
of partitions per node and the path length were plotted against the
total numbers of peers in the system. For a network with 1000 nodes,
the hop count distribution was very similar to that of the exact-
matching Chord scheme. Was it load-balanced? For the same network
with 50,000 partitions, there were over two orders of magnitude
variation in the number of partitions at each node (first and
ninety-ninth percentiles). The Prefix Hash Tree is a trie in which
prefixes are hashed onto any DHT. The preliminary analysis suggests
efficient doubly logarithmic lookup, balanced load, and fault
resilience [78, 79]. Andrzejak and Xu were perhaps the first to
propose a mapping from ranges to DHTs [80]. They use one particular
Space Filling Curve, the Hilbert curve, over a Content Addressable
Network (CAN) construction (Section 3.5.3). They maintain two
properties: nearby ranges map to nearby CAN zones; if a range is
split into two sub-ranges, then the zones of the sub-ranges partition
the zone of the primary range. They plot path length and load proxy
measures (the total number of messages and nodes visited) for three
algorithms to propagate range queries: brute force, controlled
flooding, and directed controlled flooding. Schmidt and Parashar
also advocated Space Filling Curves to achieve range queries over a
DHT [81]. However, they point out that, while Andrzejak and Xu use
an inverse Space Filling Curve to map a one-dimensional space to d-
dimensional zones, they map a d-dimensional space back to a one-
dimensional index. Such a construction gives the ability to search
across multiple attributes (Section 5.2). Tanin, Harwood, et al.
suggested quadtrees over Chord [82], and gave preliminary simulation
results for query response times.
Because DHTs are naturally constrained to exact-match, single-key
queries, researchers have considered other P2P indexes for range
searches. Several were based on Skip Graphs [38, 41], which, unlike
the DHTs, do not necessitate randomizing hash functions and are
therefore capable of range searches. Unfortunately, they are not
load balanced [83]. For example, in SkipNet [48], hashing was added
to balance the load -- the Skip Graph could support range searches or
load balancing, but not both. One solution for load-balancing relies
on an increased number of 'virtual' servers [168] but, in their
search for a system that can both search for ranges and balance
loads, Bharambe, Agrawal, et al. rejected the idea [84]. The virtual
servers work assumed load imbalance stems from hashing; that is, by
skewed data insertions and deletions. In some situations, the
imbalance is triggered by a skewed query load. In such
circumstances, additional virtual servers can increase the number of
routing hops and increase the number of pointers that a Skip Graph
needs to maintain. Ganesan, Bawa, et al. devised an alternate method
to balance load [83]. They proposed two Skip Graphs, one to index
the data itself and the other to track load at each node in the
system. Each node is able to determine the load on its neighbours
and the most (least) loaded nodes in the system. They devise two
algorithms: NBRADJUST balances load on neighbouring nodes; using
REORDER, empty nodes can take over some of the tuples on heavily
loaded nodes. Their simulations focus on skewed storage load, rather
than on skewed query loads, but they surmise that the same approach
could be used for the latter.
Other proposals for range queries avoid both the DHT and the Skip
Graph. Bharambe, Agrawal, et al. distinguish their Mercury design by
its support for multi-attribute range queries and its explicit load
balancing [84]. In Mercury, nodes are grouped into routing hubs,
each of which is responsible for various query attributes. While it
does not use hashing, Mercury is loosely similar to the DHT
approaches: nodes within hubs are arranged into rings, like Chord
[34]; for efficient routing within hubs, k long-distance links are
used, like Symphony [381]. Range lookups require O(((log n)^2)/k)
hops. Random sampling is used to estimate the average load on nodes
and to find the parts of the overlay that are lightly loaded.
Whereas Symphony assumed that nodes are responsible for ranges of
approximately equal size, Mercury's random sampling can determine the
location of the start of the range, even for non-uniform ranges [84].
P-Grid [42] does provide for range queries, by virtue of the key
ordering in its tree structures. Ganesan, Bawa, et al. critiqued its
capabilities [83]: P-Grid assumes fixed-capacity nodes; there was no
formal characterization of imbalance ratios or balancing costs; every
P-Grid periodically contacts other nodes for load information.
The work on Scalable Distributed Data Structures (SDDSs) has
progressed in parallel with P2P work and has addressed range queries.
Like the DHTs above, the early SDDS Linear Hashing (LH*) schemes were
not order-preserving [52]. To facilitate range queries, Litwin,
Niemat, et al. devised a Range Parititioning variant, RP* [87].
There are options to dispense with the index, to add indexes to
clients, and to add them to servers. In the variant without an
index, every query is issued via multicasting. The other variants
also use some multicasting. The initial RP* paper suggested
scalability to thousands of sites, but a more recent RP* simulation
was capped at 140 servers [88]. In that work, Tsangou, Ndiaye, et
al. investigated TCP and UDP mechanisms by which servers could return
range query results to clients. The primary metrics were search and
response times. Amongst the commercial parallel database management
systems, they reported that the largest seems only to scale to 32
servers (SQL Server 2000). For future work, they planned to explore
aggregation of query results, rather than establishing a connection
between the client and every single server with a response.
All in all, it seems there are numerous open research questions on
P2P range queries. How realistic is the maintenance of global load
statistics considering the scale and dynamism of P2P networks?
Simulations at larger scales are required. Proposals should take
into account both the storage load (insert and delete messages) and
the query load (lookup messages). Simplifying assumptions need to be
attacked. For example, how well do the above solutions work in
networks with heterogeneous nodes, where the maximum message loads
and index sizes are node-dependent?
5.2. Multi-Attribute Queries
There has been some work on multi-attribute P2P queries. As late as
September 2003, it was suggested that there has not been an efficient
solution [76].
Again, an early significant work on multi-attribute queries over
aggregated commodity nodes germinated amongst SDDSs. k-RP* [89] uses
the multi-dimensional binary search tree (or k-d tree, where k
indicates the number of dimensions of the search index) [384]. It
builds on the RP* work from the previous section and inherits their
capabilities for range search and partial match. Like the other
SDDSs, k-RP* indexes can fit into RAM for very fast lookup. For
future work, Litwin and Neimat suggested a) a formal analysis of the
range search termination algorithm and the k-d paging algorithm, b) a
comparison with other multi-attribute data structures (quad-trees and
R-trees) and c) exploration of query processing, concurrency control,
and transaction management for k-RP* files [89]. On the latter
point, others have considered transactions to be inconsequential to
the core problem of supporting more complex queries in P2P networks
[72].
In architecting their secure wide-area Service Discovery Service
(SDS), Hodes, Czerwinski, et al. considered three possible designs
for multi-criteria search -- Centralization, Mapping and Flooding
[90]. These correlate to the index classifications of Section 2 --
Central, Distributed, and Local. They discounted the centralized,
Napster-like index for its risk of a single point of failure. They
considered the hash-based mappings of Section 3, but concluded that
it would not be possible to adequately partition data. A document
satisfying many criteria would be wastefully stored in many
partitions. They rejected full flooding for its lack of scalability.
Instead, they devised a query filtering technique, reminiscent of
Gnutella's query routing protocol (Section 4.1). Nodes push
proactive summaries of their data rather than waiting for a query.
Summaries are aggregated and stored throughout a server hierarchy, to
guide subsequent queries. Some initial prototype measurements were
provided for total load on the system, but not for load distribution.
They put several issues forward for future work. The indexing needs
to be flexible to change according to query and storage workloads. A
mesh topology might improve on their hierarchic topology since query
misses would not propagate to root servers. The choice is analogous
to BGP meshes and DNS trees.
More recently, Cai, Frank, et al. devised the Multi-Attribute
Addressable Network (MAAN) [91]. They built on Chord to provide both
multi-attribute and range queries, claiming to be the first to
service both query types in a structured P2P system. Each MAAN node
has O(log n) neighbours, where N is the number of nodes. MAAN
multi-attribute range queries require O(log n+N*Smin) hops, where
Smin is the minimum range selectivity across all attributes.
Selectivity is the ratio of the query range to the entire identifier
range. The paper assumed that a locality preserving hash function
would ensure balanced load. Per Section 5.1, the arguments by
Bharambe, Agrawal, et al. have highlighted the shortcomings of this
assumption [84]. MAAN required that the schema must be fixed and
known in advance -- adaptable schemas were recommended for subsequent
attention. The authors also acknowledged that there is a selectivity
breakpoint at which full flooding becomes more efficient than their
scheme. This begs for a query resolution algorithm that adapts to
the profile of queries. Cai and Frank followed up with RDFPeers
[55]. They differentiate their work from other RDF proposals by a)
guaranteeing to find query results if they exist and b) removing the
requirement of prior definition of a fixed schema. They hashed
<subject, predicate, object> triples onto the MAAN and reported
routing hop metrics for their implementation. Load imbalance across
nodes was reduced to less than one order of magnitude, but the
specific measure was the number of triples stored per node - skewed
query loads were not considered. They plan to improve load balancing
with the virtual servers of Section 5.1 [168].
5.3. Join Queries
Two research teams have done some initial work on P2P join
operations. Harren, Hellerstein, et al. initially described a
three-layer architecture -- storage, DHT and query processing. They
implemented the join operation by modifying an existing Content
Addressable Network (CAN) simulator, reporting "significant hot-spots
in all dimensions: storage, processing, and routing" [72]. They
progressed their design more recently in the context of PIER, a
distributed query engine based on CAN [22, 385]. They implemented
two equi-join algorithms. In their design, a key is constructed from
the "namespace" and the "resource ID". There is a namespace for each
relation and the resource ID is the primary key for base tuples in
that relation. Queries are multicast to all nodes in the two
namespaces (relations) to be joined. Their first algorithm is a DHT
version of the symmetric hash join. Each node in the two namespaces
finds the relevant tuples and hashes them to a new query namespace.
The resource ID in the new namespace is the concatenation of join
attributes. In the second algorithm, called "fetch matches", one of
the relations is already hashed on the join attributes. Each node in
the second namespace finds tuples matching the query and retrieves
the corresponding tuples from the first relation. They leveraged two
other techniques, namely the symmetric semi-join rewrite and the
Bloom filter rewrite, to reduce the high bandwidth overheads of the
symmetric hash join. For an overlay of 10,000 nodes, they simulated
the delay to retrieve tuples and the aggregate network bandwidth for
these four schemes. The initial prototype was on a cluster of 64
PCs, but it has more recently been expanded to PlanetLab.
Triantafillou and Pitoura considered multicasting to large numbers of
peers to be inefficient [76]. They therefore allocated a limited
number of special peers, called range guards. The domain of the join
attributes was divided, one partition per range guard. Join queries
were sent only to range guards, where the query was executed.
Efficient selection of range guards and a quantitive evaluation of
their proposal were left for future work.
5.4. Aggregation Queries
Aggregation queries invariable rely on tree-structures to combine
results from a large number of nodes. Examples of aggregation
queries are Count, Sum, Maximum, Minimum, Average, Median, and Top-K
[92, 386, 387]. Figure 5 summarizes the tree and query
characteristics that affect dependability.
Tree type: Doesn't use DHT [92], use internal DHT trees [95], use
independent trees on top of DHTs
Tree repair: Periodic [93], exceptional [32]
Tree count: One per key, one per overlay [56]
Tree flexibility: Static [92], dynamic
Query interface: install, update, probe [98]
Query distribution: multicast [98], gossip [92]
Query applications: leader election, voting, resource location,
object placement and error recovery [98, 388]
Query semantics
Consistency: Best-effort, eventual [92], snapshot / interval /
single-site validity [99]
Timeliness [388]
Lifetime: Continuous [97, 99], single-shot
No. attributes: Single, multiple
Query types: Count, sum, maximum, minimum, average, median, top k
[92, 386, 387]
Figure 5: Aggregation Trees and Queries in P2P Networks
Key: Astrolabe [92]; Cone [93]; Distributed Approximative System
Information Service (DASIS) [95]; Scalable Distributed Information
Management System (SDIMS) [98]; Self-Organized Metadata Overlay
(SOMO) [56]; Wildfire [99]; Willow [32]; Newscast [97]
The fundamental design choices for aggregation trees relate to how
the overlay uses DHTs, how it repairs itself when there are failures,
how many aggregation trees there are, and whether the tree is static
or dynamic (Figure 5). Astrolabe is one of the most influential P2P
designs included in Figure 5, yet it makes no use of DHTs [92].
Other designs make use of the internal trees of Plaxton-like DHTs.
Others build independent tree structures on top of DHTs. Most of the
designs repair the aggregation tree with periodic mechanisms similar
to those used in the DHTs themselves. Willow is an exception [32].
It uses a Tree Maintenance Protocol to "zip" disjoint aggregation
trees together when there are major failures. Yalagandula and Dahlin
found reconfigurations at the aggregation layer to be costly,
suggesting more research on techniques to reduce the cost and
frequency of such reconfigurations [98]. Many of the designs use
multiple aggregation trees, each rooted at the DHT node responsible
for the aggregation attribute. On the other hand, the Self-Organized
Metadata Overlay [56] uses a single tree and is vulnerable to a
single point of failure at its root.
At the time of writing, researchers have just begun exploring the
performance of queries in the presence of churn. Most designs are
for best-effort queries. Bawa, et al. devised a better consistency
model, called Single-Site Validity [99] to qualify the accuracy of
results when there is churn. Its price was a five-fold increase in
the message load, when compared to an efficient but best-effort
Spanning Tree. Gossip mechanisms are resilient to churn, but they
delay aggregation results and incur high message cost for aggregation
attributes with small read-to-write ratios.
6. Security Considerations
An initial list of references to research on P2P security is given in
Figure 1, Section 1. This document addresses P2P search. P2P
storage, security, and applications are recommended for further
investigation in Section 8.
7. Conclusions
Research on peer-to-peer networks can be divided into four categories
-- search, storage, security and applications. This critical survey
has focused on search methods. While P2P networks have been
classified by the existence of an index (structured or unstructured)
or the location of the index (local, centralized, and distributed),
this survey has shown that most have evolved to have some structure,
whether it is indexes at superpeers or indexes defined by DHT
algorithms. As for location, the distributed index is most common.
The survey has characterized indexes as semantic and semantic-free.
It has also critiqued P2P work on major query types. While much of
it addresses work from 2000 or later, we have traced important
building blocks from the 1990s.
The initial motivation in this survey was to answer the question,
"How robust are P2P search networks?" The question is key to the
deployment of P2P technology. Balakrishnan, Kaashoek, et al. argued
that the P2P architecture is appealing: the startup and growth
barriers are low; they can aggregate enormous storage and processing
resources; "the decentralized and distributed nature of P2P systems
gives them the potential to be robust to faults or intentional
attacks" [18]. If P2P is to be a disruptive technology in
applications other than casual file sharing, then robustness needs to
be practically verified [20].
The best comparative research on P2P dependability has been done in
the context of Distributed Hash Tables (DHTs) [291]. The entire body
of DHT research can be distilled to four main observations about
dependability (Section 3.2). Firstly, static dependability
comparisons show that no O(log n) DHT geometry is significantly more
dependable than the other O(log n) geometries. Secondly, dynamic
dependability comparisons show that DHT dependability is sensitive to
the underlying topology maintenance algorithms (Figure 2). Thirdly,
most DHTs use O(log n) geometries to suit ephemeral nodes, whereas
the O(1) hop DHTs suit stable nodes - they deserve more research
attention. Fourthly, although not yet a mature science, the study of
DHT dependability is helped by recent simulation tools that support
multiple DHTs [299].
We make the following four suggestions for future P2P research:
1) Complete the companion P2P surveys for storage, security, and
applications. A rough outline has been suggested in Figure 1,
along with references. The need for such surveys was highlighted
within the peer-to-peer research group of the Internet Research
Task Force (IRTF) [17].
2) P2P indexes are maturing. P2P queries are embryonic. Work on
more expressive queries over P2P indexes started to gain momentum
in 2003, but remains fraught with efficiency and load issues.
3) Isolate the low-level mechanisms affecting robustness. There is
limited value in comparing robustness of DHT geometries (like
rings versus de Bruijn graphs), when robustness is highly
sensitive to underlying topology maintenance algorithms (Figure
2).
4) Build consensus on robustness metrics and their acceptable ranges.
This paper has teased out numerous measures that impinge on
robustness, for example, the median query path length for a
failure of x% of nodes, bisection width, path overlap, the number
of alternatives available for the next hop, lookup latency,
average live bandwidth (bytes/node/sec), successful routing rates,
the number of timeouts (caused by a finger pointing to a departed
node), lookup failure rates (caused by nodes that temporarily
point to the wrong successor during churn), and clustering
measures (edge expansion and node expansion). Application-level
robustness metrics need to drive a consistent assessment of the
underlying search mechanics.
8. Acknowledgments
This document was adapted from a paper in Elsevier's Computer
Networks:
J. Risson & T. Moors, Survey of Research towards Robust Peer-to-
Peer Networks: Search Methods, Computer Networks 51(7)2007.
We thank Bill Yeager, Ali Ghodsi, and several anonymous reviewers for
thorough comments that significantly improved the quality of earlier
versions of this document.
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Author's Addresses
John Risson
School of Elec Eng and Telecommunications
University of New South Wales
Sydney NSW 2052 Australia
EMail: jr@tuffit.com
Tim Moors
School of Elec Eng and Telecommunications
University of New South Wales
Sydney NSW 2052 Australia
EMail: t.moors@unsw.edu.au
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