| Rfc | 7945 |
|---|
| Title | Information-Centric Networking: Evaluation and Security
Considerations |
|---|
| Author | K. Pentikousis, Ed., B. Ohlman, E. Davies, S.
Spirou, G. Boggia |
|---|
| Date | September 2016 |
|---|
| Format: | TXT, HTML |
|---|
| Status: | INFORMATIONAL |
|---|
|
Internet Research Task Force (IRTF) K. Pentikousis, Ed.
Request for Comments: 7945 Travelping
Category: Informational B. Ohlman
ISSN: 2070-1721 Ericsson
E. Davies
Trinity College Dublin
S. Spirou
Intracom Telecom
G. Boggia
Politecnico di Bari
September 2016
Information-Centric Networking: Evaluation and Security Considerations
Abstract
This document presents a number of considerations regarding
evaluating Information-Centric Networking (ICN) and sheds some light
on the impact of ICN on network security. It also surveys the
evaluation tools currently available to researchers in the ICN area
and provides suggestions regarding methodology and metrics.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Research Task Force
(IRTF). The IRTF publishes the results of Internet-related research
and development activities. These results might not be suitable for
deployment. This RFC represents the consensus of the <insert_name>
Research Group of the Internet Research Task Force (IRTF). Documents
approved for publication by the IRSG are not a candidate for any
level of Internet Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
http://www.rfc-editor.org/info/rfc7945.
Copyright Notice
Copyright (c) 2016 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Evaluation Considerations . . . . . . . . . . . . . . . . . . 4
2.1. Topology Selection . . . . . . . . . . . . . . . . . . . . 5
2.2. Traffic Load . . . . . . . . . . . . . . . . . . . . . . . 6
2.3. Choosing Relevant Metrics . . . . . . . . . . . . . . . . 10
2.3.1. Traffic Metrics . . . . . . . . . . . . . . . . . . . 13
2.3.2. System Metrics . . . . . . . . . . . . . . . . . . . . 14
2.4. Resource Equivalence and Trade-Offs . . . . . . . . . . . 16
3. ICN Security Aspects . . . . . . . . . . . . . . . . . . . . . 16
3.1. Authentication . . . . . . . . . . . . . . . . . . . . . . 17
3.2. Authorization, Access Control, and Logging . . . . . . . . 18
3.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4. Changes to the Network Security Threat Model . . . . . . . 20
4. Evaluation Tools . . . . . . . . . . . . . . . . . . . . . . . 21
4.1. Open-Source Implementations . . . . . . . . . . . . . . . 21
4.2. Simulators and Emulators . . . . . . . . . . . . . . . . . 22
4.2.1. ndnSIM . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.2. ccnSIM . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.3. Icarus Simulator . . . . . . . . . . . . . . . . . . . 23
4.3. Experimental Facilities . . . . . . . . . . . . . . . . . 24
4.3.1. Open Network Lab (ONL) . . . . . . . . . . . . . . . . 24
4.3.2. POINT Testbed . . . . . . . . . . . . . . . . . . . . 25
4.3.3. CUTEi: Container-Based ICN Testbed . . . . . . . . . . 25
5. Security Considerations . . . . . . . . . . . . . . . . . . . 25
6. Informative References . . . . . . . . . . . . . . . . . . . . 26
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 37
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 38
1. Introduction
Information-Centric Networking (ICN) is a networking concept that
arose from the desire to align the operation model of a network with
the model of its typical use. For TCP/IP networks, this implies
changing the mechanisms of data access and transport from a host-to-
host model to a user-to-information model. The premise is that the
effort invested in changing models will be offset, or even surpassed,
by the potential of a "better" network. However, such a claim can be
validated only if it is quantified.
Different ICN approaches are evaluated in the peer-reviewed
literature using a mixture of theoretical analysis, simulation and
emulation techniques, and empirical (testbed) measurements. The
specific methodology employed may depend on the experimentation goal,
e.g., whether one wants to evaluate scalability, quantify resource
utilization, or analyze economic incentives. In addition, though, we
observe that ease and convenience of setting up and running
experiments can sometimes be a factor in published evaluations. As
discussed in [RFC7476], the development phase that ICN is going
through and the plethora of approaches to tackle the hardest problems
make this a very active and growing research area but, on the
downside, it also makes it more difficult to compare different
proposals on an equal footing.
Performance evaluation using actual network deployments has the
advantage of realistic workloads and reflects the environment where
the service or protocol is to be deployed. In the case of ICN,
however, it is not currently clear what qualifies as a "realistic
workload". Trace-based analysis of ICN is in its infancy, and more
work is needed towards defining characteristic workloads for ICN
evaluation studies. Accordingly, the experimental process and the
evaluation methodology per se are actively being researched for
different ICN architectures. Numerous factors affect the
experimental results, including the topology selected; the background
traffic that an application is being subjected to; network conditions
such as available link capacities, link delays, and loss-rate
characteristics throughout the selected topology; failure and
disruption patterns; node mobility; and the diversity of devices
used.
The goal of this document is to summarize evaluation guidelines and
tools alongside suggested data sets and high-level approaches. We
expect this to be of interest to the ICN community as a whole, as it
can assist researchers and practitioners alike to compare and
contrast different ICN designs, as well as with the state of the art
in host-centric solutions, and identify the respective strengths and
weaknesses. We note that, apart from the technical evaluation of the
functionality of an ICN architecture, the future success of ICN will
be largely driven by its deployability and economic viability.
Therefore, ICN evaluations should assess incremental deployability in
the existing network environment together with a view of how the
technical functions will incentivize deployers to invest in the
capabilities that allow the architecture to spread across the
network.
This document has been produced by the IRTF Information-Centric
Networking Research Group (ICNRG). The main objective of the ICNRG
is to couple ongoing ICN research in the above areas with solutions
that are relevant for evolving the Internet at large. The ICNRG
produces documents that provide guidelines for experimental
activities in the area of ICN so that different, alternative
solutions can be compared consistently, and information sharing can
be accomplished for experimental deployments. This document
incorporates input from ICNRG participants and their corresponding
text contributions; it has been reviewed by several ICNRG active
participants (see the Acknowledgments), and represents the consensus
of the research group. That said, note that this document does not
constitute an IETF standard; see also [RFC5743].
The remainder of this document is organized as follows. Section 2
presents various techniques and considerations for evaluating
different ICN architectures. Section 3 discusses the impact of ICN
on network security. Section 4 surveys the tools currently available
to ICN researchers.
2. Evaluation Considerations
It is clear that the way we evaluate IP networks will not be directly
applicable to evaluating ICN. In IP, the focus is on the performance
and characteristics of end-to-end connections between a source and a
destination. In ICN, the "source" responding to a request can be any
ICN node in the network and may change from request to request. This
makes it difficult to use concepts like delay and throughput in a
traditional way. In addition, evaluating resource usage in ICN is a
more complicated task, as memory used for caching affects delays and
use of transmission resources; see the discussion on resource
equivalents in Section 2.4.
There are two major types of evaluations of ICN that we see a need to
make. One type is to compare ICN to traditional networking, and the
other type is to compare different ICN implementations and approaches
against each other.
In this section, we detail some of the functional components needed
when evaluating different ICN implementations and approaches.
2.1. Topology Selection
There's a wealth of earlier work on topology selection for simulation
and performance evaluation of host-centric networks. While the
classic dumbbell topology is regarded as inappropriate for ICN, most
ICN studies so far have been based on that earlier work for host-
centric networks [RFC7476]. However, there is no single topology
that can be used to easily evaluate all aspects of ICN. Therefore,
one should choose from a range of topologies depending on the focus
of the evaluation.
For scalability and resilience studies, there is a wide range of
synthetic topologies, such as the Barabasi-Albert model [Barabasi99]
and the Watts-Strogatz small-world topology [Watts98]. These allow
experiments to be performed whilst controlling various key parameters
(e.g., node degree). These synthetic topologies are appropriate in
the general case, as there are no practical assurances that a future
information-centric network will have the same topology as any of
today's networks.
When studies look at cost (e.g., transit cost) or migration to ICN,
realistic topologies should be used. These can be inferred from
Internet traces, such as the CAIDA Macroscopic Internet Topology Data
Kit (http://www.caida.org/data/active/internet-topology-data-kit) and
Rocketfuel
(http://www.cs.washington.edu/research/networking/rocketfuel). A
problem is the large size of the topology (approximately 45K
Autonomous Systems, close to 200K links), which may limit the
scalability of the employed evaluation tool. Katsaros et al.
[Katsaros15] address this problem by using scaled down topologies
created following the methodology described in [Dimitropoulos09].
Studies that focus on node or content mobility can benefit from
topologies and their dynamic aspects as used in the Delay-Tolerant
Networking (DTN) community. As mentioned in [RFC7476], DTN traces
are available to be used in such ICN evaluations.
As with host-centric topologies, defining just a node graph will not
be enough for most ICN studies. The experimenter should also clearly
define and list the respective matrices that correspond to the
network, storage, and computation capacities available at each node
as well as the delay characteristics of each link [Montage]. Real
values for such parameters can be taken from existing platforms such
as iPlane (http://iplane.cs.washington.edu). Synthetic values could
be produced with specific tools [Kaune09].
2.2. Traffic Load
In this subsection, we provide a set of common guidelines, in the
form of what we will refer to as a content catalog for different
scenarios. This catalog, which is based on previously published
work, can be used to evaluate different ICN proposals, for instance,
on routing, congestion control, and performance, and can be
considered as other kinds of ICN contributions emerge. As we are
still lacking ICN-specific traffic workloads, we can currently only
extrapolate from today's workloads. A significant challenge then
relates to the identification of the applications contributing to the
observed traffic (e.g., Web or peer-to-peer), as well as to the exact
amount of traffic they contribute to the overall traffic mixture.
Efforts in this direction can take heed of today's traffic mix
comprising Web, peer-to-peer file sharing, and User-Generated Content
(UGC) platforms (e.g., YouTube), as well as Video on Demand (VoD)
services. Publicly available traces for these include those from web
sites such as the MultiProbe Framework
<http://multiprobe.ewi.tudelft.nl/multiprobe.html>,
<http://an.kaist.ac.kr/traces/IMC2007.html> (see also [Cha07]), and
the UMass Trace Repository
<http://traces.cs.umass.edu/index.php/Network/Network>.
Taking a more systematic approach, and with the purpose of modeling
the traffic load, we can resort to measurement studies that
investigate the composition of Internet traffic, such as [Labovitz10]
and [Maier09]. In [Labovitz10], a large-scale measurement study was
performed, with the purpose of studying the traffic crossing inter-
domain links. The results indicate the dominance of Web traffic,
amounting to 52% over all measured traffic. However, Deep Packet
Inspection (DPI) techniques reveal that 25-40% of all HTTP traffic
actually carries video traffic. Results from DPI techniques also
reveal the difficulty in correctly identifying the application type
in the case of P2P traffic: mapping observed port numbers to well-
known applications shows P2P traffic constituting only 0.85% of
overall traffic, while DPI raises this percentage to 18.32%
[Labovitz10]. Relevant studies on a large ISP show that the
percentage of P2P traffic ranges from 17% to 19% of overall traffic
[Maier09]. Table 1 provides an overview of these figures. The
"other" traffic type denotes traffic that cannot be classified in any
of the first three application categories, and it consists of
unclassified traffic and traffic heavily fragmented into several
applications (e.g., 0.17% DNS traffic).
Traffic Type | Ratio
=====================
Web | 31-39%
---------------------
P2P | 17-19%
---------------------
Video | 13-21%
---------------------
Other | 29-31%
=====================
Table 1: Traffic Type Ratios of Total Traffic [Labovitz10] [Maier09]
The content catalog for each type of traffic can be characterized by
a specific set of parameters:
a) the cardinality of the estimated content catalog
b) the size of the exchanged contents (either chunks or entire named
information objects)
c) the popularity of objects (expressed in their request frequency)
In most application types, the popularity distribution follows some
power law, indicating that a small number of information items
trigger a large proportion of the entire set of requests. The exact
shape of the power law popularity distribution directly impacts the
performance of the underlying protocols. For instance, highly skewed
popularity distributions (e.g., a Zipf-like distribution with a high
slope value) favor the deployment of caching schemes, since caching a
very small set of information items can dramatically increase the
cache hit ratio.
Several studies in the past few years have stated that Zipf's law is
the discrete distribution that best represents the request frequency
in a number of application scenarios, ranging from the Web to VoD
services. The key aspect of this distribution is that the frequency
of a content request is inversely proportional to the rank of the
content itself, i.e., the smaller the rank, the higher the request
frequency. If M denotes the content catalog cardinality and 1 <= i
<= M denotes the rank of the i-th most popular content, we can
express the probability of requesting the content with rank "i" as:
P(X=i) = (1 / i^(alpha)) / C, with C = SUM(1 / j^(alpha)), alpha > 0
where the sum is obtained considering all values of j, 1 <= j <= M.
A recent analysis of HTTP traffic showed that content popularity is
better reflected by a trimodal distribution model in which the head
and tail of a Zipf distribution (with slope value 0.84) are replaced
by two discrete Weibull distributions with shape parameter values 0.5
and 0.24, respectively [IMB2014].
A variation of the Zipf distribution, termed the Mandelbrot-Zipf
distribution was suggested [Saleh06] to better model environments
where nodes can locally store previously requested content. For
example, it was observed that peer-to-peer file-sharing applications
typically exhibited a 'fetch-at-most-once' style of behavior. This
is because peers tend to persistently store the files they download,
a behavior that may also be prevalent in ICN.
Popularity can also be characterized in terms of:
a) The temporal dynamics of popularity, i.e., how requests are
distributed in time. The popularity distribution expresses the
number of requests submitted for each information item
participating into a certain workload. However, they do not
describe how these requests are distributed in time. This aspect
is of primary importance when considering the performance of
caching schemes since the ordering of the requests obviously
affects the contents of a cache. For example, with a Least
Frequently Used (LFU) cache replacement policy, if all requests
for a certain item are submitted close in time, the item is
unlikely to be evicted from the cache, even by a (globally) more
popular item whose requests are more evenly distributed in time.
The temporal ordering of requests gains even more importance when
considering workloads consisting of various applications, all
competing for the same cache space.
b) The spatial locality of popularity i.e., how requests are
distributed throughout a network. The importance of spatial
locality relates to the ability to avoid redundant traffic in the
network. If requests are highly localized in some area of the
entire network, then similar requests can be more efficiently
served with mechanisms such as caching and/or multicast, i.e., the
concentration of similar requests in a limited area of the network
allows increasing the perceived cache hit ratios at caches in the
area and/or the traffic savings from the use of multicast.
Table 2 provides an overview of distributions that can be used to
model each of the identified traffic types i.e., Web, Video (based
on YouTube measurements), and P2P (based on BitTorrent
measurements). These distributions are the outcome of a series of
modeling efforts based on measurements of real traffic workloads
([Breslau99] [Mahanti00] [Busari02] [Arlitt97] [Barford98]
[Barford99] [Hefeeda08] [Guo07] [Bellissimo04] [Cheng08]
[Cheng13]). A tool for the creation of synthetic workloads
following these models, and also allowing the generation of
different traffic mixes, is described in [Katsaros12].
| Object Size | Temporal Locality | Popularity Distribution
=====================================================================
Web | Concatenation | Ordering via the | Zipf: p(i)=K/i^a
| of Lognormal | Least Recently Used | i: popularity rank
| (body) and | (LRU) stack model | N: total items
| Pareto (tail) | [Busari02] | K: 1/Sum(1/i^a)
| [Barford98] | | a: distribution slope
| [Barford99] | Exact timing via | values 0.64-0.84
| | exponential | [Breslau99] [Mahanti00]
| | distribution |
| | [Arlitt97] |
---------------------------------------------------------------------
VoD | Duration/size: | No analytical models | Weibull: k=0.513,
| Concatenated | | lambda=6010
| normal; most | Random distribution |
| videos | across total | Gamma: k=0.372,
| ~330 kbit/s | duration | theta=23910
| [Cheng13] | | [Cheng08]
---------------------------------------------------------------------
P2P | Wide variation | Mean arrival rate of | Mandelbrot-Zipf
| on torrent | 0.9454 torrents/hour | [Hefeeda08]:
| sizes | Peers in a swarm | p(i)=K/((i+q)/a)
| [Hefeeda08]. | arrive as | q: plateau factor,
| No analytical | l(t)= l0*e^(-t/tau) | 5 to 100.
| models exist: | l0: initial arrival | Flatter head than in
| Sample a real | rate (87.74 average) | Zipf-like distribution
| BitTorrent | tau: object | (where q=0)
| distribution | popularity |
| [Bellissimo04] | (1.16 average)* |
| or use fixed | [Guo07] |
| value | |
=====================================================================
* Random ordering of swarm births (first request). For each swarm,
calculate a different tau. Based on average tau and object
popularity. Exponential decay rule for subsequent requests.
Table 2: Overview of Traffic Type Models
Table 3 summarizes the content catalog. With this shared point of
reference, the use of the same set of parameters (depending on the
scenario of interest) among researchers will be eased, and different
proposals could be compared on a common base.
Traffic | Catalog | Mean Object Size | Popularity Distribution
Load | Size | [Zhou11] [Fri12] | [Cha07] [Fri12] [Yu06]
|[Goog08] | [Marciniak08] | [Breslau99] [Mahanti00]
|[Zhang10a]| [Bellissimo04] |
|[Cha07] | [Psaras11] |
|[Fri12] | [Carofiglio11] |
| | |
| | |
| | |
===================================================================
Web | 10^12 | Chunk: 1-10 KB | Zipf with
| | | 0.64 <= alpha <= 0.83
-------------------------------------------------------------------
File | 5x10^6 | Chunk: 250-4096 KB | Zipf with
sharing | | Object: ~800 MB | 0.75 <= alpha <= 0.82
-------------------------------------------------------------------
UGC | 10^8 | Object: ~10 MB | Zipf, alpha >= 2
-------------------------------------------------------------------
VoD | 10^4 | Object: ~100 MB | Zipf, 0.65 <= alpha <= 1
(+HLS) | | ~1 KB (*) |
(+DASH) | | ~5.6 KB (*) |
===================================================================
UGC = User-Generated Content
VoD = Video on Demand
HLS = HTTP Live Streaming
DASH = Dynamic Adaptive Streaming over HTTP
(*) Using adaptive video streaming (e.g., HLS and DASH), with an
optimal segment length (10 s for HLS and 2 s for DASH) and a
bitrate of 4500 kbit/s [RFC7933] [Led12]
Table 3: Content Catalog
2.3. Choosing Relevant Metrics
Quantification of network performance requires a set of standard
metrics. These metrics should be broad enough so they can be applied
equally to host-centric and information-centric (or other) networks.
This will allow reasoning about a certain ICN approach in relation to
an earlier version of the same approach, to another ICN approach, or
to the incumbent host-centric approach. It will therefore be less
difficult to gauge optimization and research direction. On the other
hand, the metrics should be targeted to network performance only and
should avoid unnecessary expansion into the physical and application
layers. Similarly, at this point, it is more important to capture as
metrics only the main figures of merit and to leave more esoteric and
less frequent cases for the future.
To arrive at a set of relevant metrics, it would be beneficial to
look at the metrics used in existing ICN approaches, such as Content-
Centric Networking (CCN) [Jacobson09] [VoCCN] [Zhang10b], NetInf
[4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-B3], PURSUIT [PRST4.5], COMET
[CMT-D5.2] [CMT-D6.2], Connect [Muscariello11] [Perino11], and
CONVERGENCE [Detti12] [Blefari-Melazzi12] [Salsano12]. The metrics
used in these approaches fall into two categories: metrics for the
approach as a whole, and metrics for individual components (name
resolution, routing, and so on). Metrics for the entire approach are
further subdivided into traffic and system metrics. It is important
to note that the various approaches do not name or define metrics
consistently. This is a major problem when trying to find metrics
that allow comparison between approaches. For the purposes of
exposition, we have tried to smooth over differences by classifying
similarly defined metrics under the same name. Also, due to space
constraints, we have chosen to report here only the most common
metrics between approaches. For more details, the reader should
consult the references for each approach.
Traffic metrics in existing ICN approaches are summarized in Table 4.
These are metrics for evaluating an approach mainly from the
perspective of the end user, i.e., the consumer, provider, or owner
of the content or service. Depending on the level where these
metrics are measured, we have made the distinction into user,
application, and network-level traffic metrics. So, for example,
network-level metrics are mostly focused on packet characteristics,
whereas user-level metrics can cover elements of human perception.
The approaches do not make this distinction explicitly, but we can
see from the table that CCN and NetInf have used metrics from all
levels, PURSUIT and COMET have focused on lower-level metrics, and
Connect and CONVERGENCE opted for higher-level metrics. Throughput
and download time seem to be the most popular metrics altogether.
User | Application | Network
======================================================
Download | Goodput | Startup | Throughput | Packet
time | | latency | | delay
==================================================================
CCN | x | x | | x | x
------------------------------------------------------------------
NetInf | x | | x | x | x
------------------------------------------------------------------
PURSUIT | | | x | x | x
------------------------------------------------------------------
COMET | | | x | x |
------------------------------------------------------------------
Connect | x | | | |
------------------------------------------------------------------
CONVERGENCE | x | x | | |
==================================================================
Table 4: Traffic Metrics Used in ICN Evaluations
While traffic metrics are more important for the end user, the owner
or operator of the networking infrastructure is normally more
interested in system metrics, which can reveal the efficiency of an
approach. The most common system metrics used are: protocol
overhead, total traffic, transit traffic, cost savings, router cost,
and router energy consumption.
Besides the traffic and systems metrics that aim to evaluate an
approach as a whole, all surveyed approaches also evaluate the
performance of individual components. Name resolution, request/data
routing, and data caching are the most typical components, as
summarized in Table 5. Forwarding Information Base (FIB) size and
path length, i.e., the routing component metrics, are almost
ubiquitous among approaches, perhaps due to the networking background
of the involved researchers. That might be also the reason for the
sometimes decreased focus on traffic and system metrics, in favor of
component metrics. It can certainly be argued that traffic and
system metrics are affected by component metrics; however, no
approach has made the relationship clear. With this in mind and
taking into account that traffic and system metrics are readily
useful to end users and network operators, we will restrict ourselves
to those in the following subsections.
Resolution | Routing | Cache
======================================================
Resolution | Request | FIB | Path | Size | Hit
time | rate | size | length | | ratio
==================================================================
CCN | x | | x | x | x | x
------------------------------------------------------------------
NetInf | x | x | | x | | x
------------------------------------------------------------------
PURSUIT | | | x | x | |
------------------------------------------------------------------
COMET | x | x | x | x | | x
------------------------------------------------------------------
CONVERGENCE | | x | x | | x |
==================================================================
Table 5: Component Metrics in Existing ICN Approaches
Before proceeding, we should note that we would like our metrics to
be applicable to host-centric networks as well. Standard metrics
already exist for IP networks, and it would certainly be beneficial
to take them into account. It is encouraging that many of the
metrics used by existing ICN approaches can also be used on IP
networks and that all of the approaches have tried on occasion to
draw the parallels.
2.3.1. Traffic Metrics
The IETF has been working for more than a decade on devising metrics
and methods for measuring the performance of IP networks. The work
has been carried out largely within the IP Performance Metrics (IPPM)
working group, guided by a relevant framework [RFC2330]. IPPM
metrics include delay, delay variation, loss, reordering, and
duplication. While the IPPM work is certainly based on packet-
switched IP networks, it is conceivable that it can be modified and
extended to cover ICN networks as well. However, more study is
necessary to turn this claim into a certainty. Many experts have
toiled for a long time on devising and refining the IPPM metrics and
methods, so it would be an advantage to use them for measuring ICN
performance. In addition, said metrics and methods work already for
host-centric networks, so comparison with information-centric
networks would entail only the ICN extension of the IPPM framework.
Finally, an important benefit of measuring the transport performance
of a network at its output, using Quality of Service (QoS) metrics
such as IPPM, is that it can be done mostly without any dependence to
applications.
Another option for measuring transport performance would be to use
QoS metrics, not at the output of the network like with IPPM, but at
the input to the application. For a live video-streaming application
the relevant metrics would be startup latency, playout lag, and
playout continuity. The benefit of this approach is that it
abstracts away all details of the underlying transport network, so it
can be readily applied to compare between networks of different
concepts (host-centric, information-centric, or other). As implied
earlier, the drawback of the approach is its dependence on the
application, so it is likely that different types of applications
will require different metrics. It might be possible to identify
standard metrics for each type of application, but the situation is
not as clear as with IPPM metrics, and further investigation is
necessary.
At a higher level of abstraction, we could measure the network's
transport performance at the application output. This entails
measuring the quality of the transported and reconstructed
information as perceived by the user during consumption. In such an
instance we would use Quality of Experience (QoE) metrics, which are
by definition dependent on the application. For example, the
standardized methods for obtaining a Mean Opinion Score (MOS) for
VoIP (e.g., ITU-T Recommendation P.800) is quite different from those
for IPTV (e.g., Perceptual Evaluation of Video Quality (PEVQ)).
These methods are notoriously hard to implement, as they involve real
users in a controlled environment. Such constraints can be relaxed
or dropped by using methods that model human perception under certain
environments, but these methods are typically intrusive. The most
important drawback of measuring network performance at the output of
the application is that only one part of each measurement is related
to network performance. The rest is related to application
performance, e.g., video coding, or even device capabilities, both of
which are irrelevant to our purposes here and are generally hard to
separate. We therefore see the use of QoE metrics in measuring ICN
performance as a poor choice at this stage.
2.3.2. System Metrics
Overall system metrics that need to be considered include
reliability, scalability, energy efficiency, and delay/disconnection
tolerance. In deployments where ICN is addressing specific
scenarios, relevant system metrics could be derived from current
experience. For example, in Internet of Things (IoT) scenarios,
which are discussed in [RFC7476], it is reasonable to consider the
current generation of sensor nodes, sources of information, and even
measurement gateways (e.g., for smart metering at homes) or
smartphones. In this case, ICN operation ought to be evaluated with
respect not only to overall scalability and network efficiency, but
also the impact on the nodes themselves. Karnouskos et al.
[SensReqs] provide a comprehensive set of sensor and IoT-related
requirements, for example, which include aspects such as resource
utilization, service life-cycle management, and device management.
Additionally, various specific metrics are also critical in
constrained environments, such as processing requirements, signaling
overhead, and memory allocation for caching procedures, in addition
to power consumption and battery lifetime. For gateways (which
typically act as a point of service to a large number of nodes and
have to satisfy the information requests from remote entities), we
need to consider scalability-related metrics, such as frequency and
processing of successfully satisfied information requests.
Finally, given the in-network caching functionality of ICNs,
efficiency and performance metrics of in-network caching have to be
defined. Such metrics will need to guide researchers and operators
regarding the performance of in-network caching algorithms. A first
step on this direction has been made in [Psaras11]. The paper
proposes a formula that approximates the proportion of time that a
piece of content stays in a network cache. The model takes as input
the rate of requests for a given piece of content (the Content of
Interest (CoI)) and the rate of requests for all other contents that
go through the given network element (router) and move the CoI down
in the (LRU) cache. The formula takes also into account the size of
the cache of this router.
The output of the model essentially reflects the probability that the
CoI will be found in a given cache. An initial study [Psaras11] is
applied to the CCN / Named Data Networking (NDN) framework, where
contents get cached at every node they traverse. The formula
according to which the probability or proportion is calculated is
given by:
pi = [mu / (mu + lambda)]^N
where lambda is the request rate for the CoI, mu is the request rate
for contents that move the CoI down the cache, and N is the size of
the cache (in slots).
The formula can be used to assess the caching performance of the
system and can also potentially be used to identify the gain of the
system due to caching. This can then be used to compare against
gains by other factors, e.g., addition of extra bandwidth in the
network.
2.4. Resource Equivalence and Trade-Offs
As we have seen above, every ICN network is built from a set of
resources, which include link capacities, and different types of
memory structures and repositories used for storing named data
objects and chunks temporarily (i.e., caching) or persistently, as
well as name resolution and other lookup services. A range of
engineering trade-offs arise from the complexity and processing
requirements of forwarding decisions, management needs (e.g., manual
configuration, explicit garbage collection), and routing needs (e.g.,
amount of state, manual configuration of routing tables, support for
mobility).
In order to be able to compare different ICN approaches, it would be
beneficial to be able to define equivalence in terms of different
resources that today are considered incomparable. For example, would
provisioning an additional 5 Mbit/s link capacity lead to better
performance than adding 100 GB of in-network storage? Within this
context, one would consider resource equivalence (and the associated
trade-offs) -- for example, for cache hit ratios per GB of cache,
forwarding decision times, CPU cycles per forwarding decision, and so
on.
3. ICN Security Aspects
The introduction of an information-centric networking architecture
and the corresponding communication paradigm results in changes to
many aspects of network security. These will affect all scenarios
described in [RFC7476]. Additional evaluation will be required to
ensure relevant security requirements are appropriately met by the
implementation of the chosen architecture in the various scenarios.
The ICN security aspects described in this document reflect the ICN
security challenges outlined in [RFC7927].
The ICN architectures currently proposed have concentrated on
authentication of delivered content to ensure its integrity. Even
though the approaches are primarily applicable to freely accessible
content that does not require access authorization, they will
generally support delivery of encrypted content.
The introduction of widespread caching mechanisms may also provide
additional attack surfaces. The caching architecture to be used also
needs to be evaluated to ensure that it meets the requirements of the
usage scenarios.
In practice, the work on security in the various ICN research
projects has been heavily concentrated on authentication of content.
Work on authorization, access control, and privacy and security
threats due to the expanded role of in-network caches has been quite
limited. For example, a roadmap for improving the security model in
NetInf can be found in [Renault09]. As secure communications on the
Internet are becoming the norm, major gaps in ICN security aspects
are bound to undermine the adoption of ICN. A comprehensive overview
of ICN security is also provided in [Tourani16].
In the following subsections, we briefly consider the issues and
provide pointers to the work that has been done on the security
aspects of the architectures proposed.
3.1. Authentication
For fully secure content distribution, content access requires that
the receiver be able to reliably assess:
validity: Is it a complete, uncorrupted copy of what was
originally published?
provenance: Can the receiver identify the publisher? If so, can it
and the source of any cached version of the document
be adequately trusted?
relevance: Is the content an answer to the question that the
receiver asked?
All ICN architectures considered in this document primarily target
the validity requirement using strong cryptographic means to tie the
content request name to the content. Provenance and relevance are
directly targeted to varying extents: There is a tussle or trade-off
between simplicity and efficiency of access and level of assurance of
all these traits. For example, maintaining provenance information
can become extremely costly, particularly when considering (historic)
relationships between multiple objects. Architectural decisions have
therefore been made in each case as to whether the assessment is
carried out by the information-centric network or left to the
application.
An additional consideration for authentication is whether a name
should be irrevocably and immutably tied to a static piece of
preexisting content or whether the name can be used to refer to
dynamically or subsequently generated content. Schemes that only
target immutable content can be less resource-hungry as they can use
digest functions rather than public key cryptography for generating
and checking signatures. However, this can increase the load on
applications because they are required to manage many names, rather
than use a single name for an item of evolving content that changes
over time (e.g., a piece of data containing an age reference).
Data-Oriented Network Architecture (DONA) [DONA] and CCN [Jacobson09]
[Smetters09] integrate most of the data needed to verify provenance
into all content retrievals but need to be able to retrieve
additional information (typically a security certificate) in order to
complete the provenance authentication. Whether the application has
any control of this extra retrieval will depend on the
implementation. CCN is explicitly designed to handle dynamic content
allowing names to be pre-allocated and attached to subsequently
generated content. DONA offers variants for dynamic and immutable
content.
Publish-Subscribe Internet Technology (PURSUIT) [Tagger12] appears to
allow implementers to choose the authentication mechanism so that it
can, in theory, emulate the authentication strategy of any of the
other architectures. It is not clear whether different choices would
lead to lack of interoperability.
NetInf uses the Named Information (ni) URI scheme [RFC6920] to
identify content. This allows NetInf to assure validity without any
additional information but gives no assurance on provenance or
relevance. A "search" request allows an application to identify
relevant content, and applications may choose to structure content to
allow provenance assurance, but this will typically require
additional network access. NetInf validity authentication is
consequently efficient in a network environment with intermittent
connectivity as it does not force additional network accesses and
allows the application to decide on provenance validation if
required. For dynamic content, NetInf can use, e.g., signed
manifests. For more details on NetInf security, see [Dannewitz10].
3.2. Authorization, Access Control, and Logging
A potentially major concern for all ICN architectures considered here
is that they do not provide any inbuilt support for an authorization
framework or for logging. Once content has been published and cached
in servers, routers, or endpoints not controlled by the publisher,
the publisher has no way to enforce access control, determine which
users have accessed the content, or revoke its publication. In fact,
in some cases (where requests do not necessarily contain host/user
identifier information), it is difficult for the publishers
themselves to perform access control.
Access could be limited by encrypting the content, but the necessity
of distributing keys out-of-band appears to negate the advantages of
in-network caching. This also creates significant challenges when
attempting to manage and restrict key access. An authorization
delegation scheme has been proposed [Fotiou12]. This scheme allows
semi-trusted entities (such as caches or CDN nodes) to delegate
access control decisions to third-party access control providers that
are trusted by the content publisher. The former entities have no
access to subscriber-related information and should respect the
decisions of the access control providers.
A recent proposal for an extra layer in the protocol stack [LIRA]
gives control of the name resolution infrastructure to the publisher.
This enables access logging as well some degree of active cache
management, e.g., purging of stale content.
One possible technique that could allow for providing access control
to heterogeneous groups and still allow for a single encrypted object
representation that remains cacheable is Attribute-Based Encryption
(ABE). A first proposal for this is presented in [Ion13]. To
support heterogeneous groups and avoid having a single authority that
has a master key multi-authority, ABE can be used [Lewko11].
Evaluating the impact of the absence of these features will be
essential for any scenario where an ICN architecture might be
deployed. It may have a seriously negative impact on the
applicability of ICN in commercial environments unless a solution can
be found.
3.3. Privacy
Another area where the architectures have not been significantly
analyzed is privacy. Caching implies a trade-off between network
efficiency and privacy. The activity of users is significantly more
exposed to the scrutiny of cache owners with whom they may not have
any relationship. However, it should be noted that it is only the
first-hop router/cache that can see who requests what, as requests
are aggregated and only the previous-hop router is visible when a
request is forwarded.
Although in many ICN architectures the source of a request is not
explicitly identified, an attacker may be able to obtain considerable
information if he or she can monitor transactions on the cache and
obtain details of the objects accessed, the topological direction of
requests, and information about the timing of transactions. The
persistence of data in the cache can make life easier for an attacker
by giving a longer timescale for analysis.
The impact of CCN on privacy has been investigated in [Lauinger10],
and the analysis is applicable to all ICN architectures because it is
mostly focused on the common caching aspect. The privacy risks of
Named Data Networking are also highlighted in [Lauinger12]. Further
work on privacy in ICNs can be found in [Chaabane13]. Finally,
Fotiou et al. define an ICN privacy evaluation framework in
[Fotiou14].
3.4. Changes to the Network Security Threat Model
The architectural differences of the various ICN models versus TCP/IP
have consequences for network security. There is limited
consideration of the threat models and potential mitigation in the
various documents describing the architectures. [Lauinger10] and
[Chaabane13] also consider the changed threat model. Some of the key
aspects are:
o Caching implies a trade-off between network efficiency and user
privacy as discussed in Section 3.3.
o More-powerful routers upgraded to handle persistent caching
increase the network's attack surface. This is particularly
the case in systems that may need to perform cryptographic
checks on content that is being cached. For example, not doing
this could lead routers to disseminate invalid content.
o ICNs makes it difficult to identify the origin of a request (as
mentioned in Section 3.3), slowing down the process of blocking
requests and requiring alternative mechanisms to differentiate
legitimate requests from inappropriate ones as access control
lists (ACLs) will probably be of little value for ICN requests.
o Denial-of-service (DoS) attacks may require more effort on ICN
than on TCP/IP-based host-centric networks, but they are still
feasible. One reason for this is that it is difficult for the
attacker to force repeated requests for the same content onto a
single node; ICNs naturally spread content so that after the
initial few requests, subsequent requests will generally be
satisfied by alternative sources, blunting the impact of a DoS
attack. That said, there are many ways around this, e.g.,
generating random suffix identifiers that always result in
cache misses.
o Per-request state in routers can be abused for DoS attacks.
o Caches can be misused in the following ways:
+ Attackers can use caches as storage to make their own
content available.
+ The efficiency of caches can be decreased by attackers with
the goal of DoS attacks.
+ Content can be extracted by any attacker connected to the
cache, putting users' privacy at risk.
Appropriate mitigation of these threats will need to be considered in
each scenario.
4. Evaluation Tools
Since ICN is an emerging area, the community is in the process of
developing effective evaluation environments, including releasing
open-source implementations, simulators, emulators, and testbeds. To
date, none of the available evaluation tools can be seen as the one
and only community reference evaluation tool. Furthermore, no single
environment supports all well-known ICN approaches, as we describe
below, hindering the direct comparison of the results obtained for
different ICN approaches. The subsections that follow review the
currently publicly available ICN implementations, simulators, and
experimental facilities.
An updated list of the available evaluation tools will be maintained
at the ICNRG Wiki page: <https://trac.tools.ietf.org/group/irtf/trac/
wiki/IcnEvaluationAndTestbeds>
4.1. Open-Source Implementations
The Named Data Networking (NDN) project has open-sourced a software
reference implementation of the architecture and protocol called NDN
(http://named-data.net). NDN is available for deployment on various
operating systems and includes C and Java libraries that can be used
to build applications.
CCN-lite (http://www.ccn-lite.net) is a lightweight implementation of
the CCN protocol that supports most of the key features of CCNx and
is interoperable with CCNx. CCN-lite implements the core CCN logic
in about 1000 lines of code, so it is ideal for classroom work and
course projects as well as for quickly experimenting with CCN
extensions. For example, Baccelli et al. use CCN-lite on top of the
RIOT operating system to conduct experiments over an IoT testbed
[Baccelli14].
PARC is offering CCN source code under various licensing schemes,
please see <http://www.ccnx.org> for details.
The PURSUIT project (http://www.fp7-pursuit.eu) has open-sourced its
Blackhawk publish-subscribe (Pub/Sub) implementation for Linux and
Android; more details are available at
<https://github.com/fp7-pursuit/blackadder>. Blackadder uses the
Click modular router for ease of development. The code distribution
features a set of tools, test applications, and scripts. The POINT
project (http://www.point-h2020.eu) is currently maintaining
Blackadder.
The 4WARD and SAIL projects have open-sourced software that
implements different aspects of NetInf, e.g., the NetInf URI format
and HTTP and UDP convergence layer, using different programming
languages. The Java implementation provides a local caching proxy
and client. Further, an OpenNetInf prototype is available as well as
a hybrid host-centric and information-centric network architecture
called the Global Information Network (GIN), a browser plug-in and
video-streaming software. See <http://www.netinf.org/open-source>
for more details.
4.2. Simulators and Emulators
Simulators and emulators should be able to capture faithfully all
features and operations of the respective ICN architecture(s) and any
limitations should be openly documented. It is essential that these
tools and environments come with adequate logging facilities so that
one can use them for in-depth analysis as well as debugging.
Additional requirements include the ability to support medium- to
large-scale experiments, the ability to quickly and correctly set
various configurations and parameters, as well as to support the
playback of traffic traces captured on a real testbed or network.
Obviously, this does not even begin to touch upon the need for strong
validation of any evaluated implementations.
4.2.1. ndnSIM
The Named Data Networking (NDN) project (http://named-data.net) has
developed ndnSIM [ndnSIM] [ndnSIM2]; this is a module that can be
plugged into the ns-3 simulator (https://www.nsnam.org) and supports
the core features of NDN. One can use ndnSIM to experiment with
various NDN applications and services as well as components developed
for NDN such as routing protocols and caching and forwarding
strategies, among others. The code for ns-3 and ndnSIM is openly
available to the community and can be used as the basis for
implementing ICN protocols or applications. For more details, see
<http://ndnsim.net/2.0/>.
4.2.2. ccnSIM
ccnSim [ccnSim] is a CCN-specific simulator that was specially
designed to handle forwarding of a large number of CCN-chunks
(http://www.infres.enst.fr/~drossi/index.php?n=Software.ccnSim).
ccnSim is written in C++ for the OMNeT++ simulation framework
(https://omnetpp.org). Other CCN-specific simulators include the CCN
Packet-Level Simulator [CCNPL] and CCN-Joker [Cianci12]. CCN-Joker
emulates in user space all basic aspects of a CCN node (e.g.,
handling of Interest and Data packets, cache sizing, replacement
policies), including both flow and congestion control. The code is
open source and is suitable for both emulation-based analyses and
real experiments. Finally, Cabral et al. [MiniCCNx] use container-
based emulation and resource isolation techniques to develop a
prototyping and emulation tool.
4.2.3. Icarus Simulator
The Icarus simulator [ICARUS] focuses on caching in ICN and is
agnostic with respect to any particular ICN implementation. The
simulator is implemented in Python, uses the Fast Network Simulator
Setup tool [Saino13], and is available at
<http://icarus-sim.github.io>. Icarus has several caching strategies
implemented, including among others ProbCache [Psaras12], node-
centrality-based caching [Chai12], and hash-route-based caching
[HASHROUT].
ProbCache [Psaras12] is taking a resource management view on caching
decisions and approximates the available cache capacity along the
path from source to destination. Based on this approximation and in
order to reduce caching redundancy across the path, it caches content
probabilistically. According to [Chai12], the node with the highest
"betweenness centrality" along the path from source to destination is
responsible for caching incoming content. Finally, [HASHROUT]
calculates the hash function of a content's name and assigns contents
to caches of a domain according to that. The hash space is split
according to the number of caches of the network. Then, upon
subsequent requests, and based again on the hash of the name included
in the request, edge routers redirect requests to the cache assigned
with the corresponding hash space. [HASHROUT] is an off-path caching
strategy; in contrast to [Psaras12] and [Chai12], it requires minimum
coordination and redirection overhead. In its latest update, Icarus
also includes implementation of the "Satisfied Interest Table" (SIT)
[Sourlas15]. The SIT points in the direction where content has been
sent recently. Among other benefits, this enables information
resilience in case of network fragmentation (i.e., content can still
be found in neighbor caches or in users' devices) and inherently
supports user-assisted caching (i.e., P2P-like content distribution).
Tortelli et al. [ICNSIMS] provide a comparison of ndnSIM, ccnSim, and
Icarus.
4.3. Experimental Facilities
An important consideration in the evaluation of any kind of future
Internet mechanism lies in the characteristics of that evaluation
itself. Central to the assessment of the features provided by a
novel mechanism is the consideration of how it improves over already
existing technologies, and by "how much". With the disruptive nature
of clean-slate approaches generating new and different technological
requirements, it is complex to provide meaningful results for a
network-layer framework, in comparison with what is deployed in the
current Internet. Thus, despite the availability of ICN
implementations and simulators, the need for large-scale environments
supporting experimental evaluation of novel research is of prime
importance to the advancement of ICN deployment.
Different experimental facilities have different characteristics and
capabilities, e.g., having low cost of use, reproducible
configuration, easy-to-use tools, and available background traffic,
and being sharable.
4.3.1. Open Network Lab (ONL)
An example of an experimental facility that supports CCN is the Open
Network Lab [ONL] that currently comprises 18 extensible gigabit
routers and over a 100 computers representing clients and is freely
available to the public for running CCN experiments. Nodes in ONL
are preloaded with CCNx software. ONL provides a graphical user
interface for easy configuration and testbed setup as per the
experiment requirements, and also serves as a control mechanism,
allowing access to various control variables and traffic counters.
Further, it is also possible to run and evaluate CCN over popular
testbeds [PLANETLAB] [EMULAB] [DETERLAB] [OFELIA] by directly
running, for example, the CCNx open-source code [Salsano13]
[Carofiglio13] [Awiphan13] [Bernardini14]. Also, the Network
Experimentation Programming Interface (NEPI) [NEPI] is a tool
developed for controlling and managing large-scale network
experiments. NEPI can be used to control and manage large-scale CCNx
experiments, e.g., on PlanetLab [Quereilhac14].
4.3.2. POINT Testbed
The POINT project is maintaining a testbed with 40 machines across
Europe, North America (Massachusetts Institute of Technology (MIT)),
and Japan (National Institute of Information and Communications
Technology (NICT)) interconnected in a topology containing one
Topology Manager and one rendezvous node that handle all
publish/subscribe and topology formation requests [Parisis13]. All
machines run Blackadder (see Section 4.1). New nodes can join, and
experiments can be run on request.
4.3.3. CUTEi: Container-Based ICN Testbed
NICT has also developed a testbed used for ICN experiments [Asaeda14]
comprising multiple servers located in Asia and other locations.
Each testbed server (or virtual machine) utilizes a Linux kernel-
based container (LXC) for node virtualization. This testbed enables
users to run applications and protocols for ICN in two
experimentation modes using two different container designs:
1. application-level experimentation using a "common container"
and
2. network-level experimentation using a "user container."
A common container is shared by all testbed users, and a user
container is assigned to one testbed user. A common container has a
global IP address to connect with other containers or external
networks, whereas each user container uses a private IP address and a
user space providing a closed networking environment. A user can
login to his/her user containers using SSH with his/her certificate,
or access them from PCs connected to the Internet using SSH
tunneling.
This testbed also implements an "on-filesystem cache" to allocate
caching data on a UNIX filesystem. The on-filesystem cache system
accommodates two kinds of caches: "individual cache" and "shared
cache." Individual cache is accessible for one dedicated router for
the individual user, while shared cache is accessible for a set of
routers in the same group to avoid duplicated caching in the
neighborhood for cooperative caching.
5. Security Considerations
This document does not impact the security of the Internet, but
Section 3 outlines security and privacy concerns that might affect a
deployment of a future ICN approach.
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Acknowledgments
Konstantinos Katsaros contributed the updated text of Section 2.2
along with an extensive set of references.
Priya Mahadevan, Daniel Corujo, and Gareth Tyson contributed to a
draft version of this document.
This document has benefited from reviews, pointers to the growing ICN
literature, suggestions, comments, and proposed text provided by the
following members of the IRTF Information-Centric Networking Research
Group (ICNRG), listed in alphabetical order: Marica Amadeo, Hitoshi
Asaeda, E. Baccelli, Claudia Campolo, Christian Esteve Rothenberg,
Suyong Eum, Nikos Fotiou, Dorothy Gellert, Luigi Alfredo Grieco,
Myeong-Wuk Jang, Ren Jing, Will Liu, Antonella Molinaro, Luca
Muscariello, Ioannis Psaras, Dario Rossi, Stefano Salsano, Damien
Saucez, Dirk Trossen, Jianping Wang, Yuanzhe Xuan, and Xinwen Zhang.
The IRSG review was provided by Aaron Falk.
Authors' Addresses
Kostas Pentikousis (editor)
Travelping
Koernerstr. 7-10
10785 Berlin
Germany
Email: k.pentikousis@travelping.com
Borje Ohlman
Ericsson Research
S-16480 Stockholm
Sweden
Email: Borje.Ohlman@ericsson.com
Elwyn Davies
Trinity College Dublin/Folly Consulting Ltd
Dublin, 2
Ireland
Email: davieseb@scss.tcd.ie
Spiros Spirou
Intracom Telecom
19.7 km Markopoulou Avenue
19002 Peania, Athens
Greece
Email: spis@intracom-telecom.com
Gennaro Boggia
Dept. of Electrical and Information Engineering
Politecnico di Bari
Via Orabona 4
70125 Bari
Italy
Email: g.boggia@poliba.it