| Rfc | 5475 |
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| Title | Sampling and Filtering Techniques for IP Packet Selection |
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| Author | T. Zseby,
M. Molina, N. Duffield, S. Niccolini, F. Raspall |
|---|
| Date | March 2009 |
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| Format: | TXT, HTML |
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| Status: | PROPOSED STANDARD |
|---|
|
Network Working Group T. Zseby
Request for Comments: 5475 Fraunhofer FOKUS
Category: Standards Track M. Molina
DANTE
N. Duffield
AT&T Labs - Research
S. Niccolini
NEC Europe Ltd.
F. Raspall
EPSC-UPC
March 2009
Sampling and Filtering Techniques for IP Packet Selection
Status of This Memo
This document specifies an Internet standards track protocol for the
Internet community, and requests discussion and suggestions for
improvements. Please refer to the current edition of the "Internet
Official Protocol Standards" (STD 1) for the standardization state
and status of this protocol. Distribution of this memo is unlimited.
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Abstract
This document describes Sampling and Filtering techniques for IP
packet selection. It provides a categorization of schemes and
defines what parameters are needed to describe the most common
selection schemes. Furthermore, it shows how techniques can be
combined to build more elaborate packet Selectors. The document
provides the basis for the definition of information models for
configuring selection techniques in Metering Processes and for
reporting the technique in use to a Collector.
Table of Contents
1. Introduction ....................................................3
1.1. Conventions Used in This Document ..........................4
2. PSAMP Documents Overview ........................................4
3. Terminology .....................................................4
3.1. Observation Points, Packet Streams, and Packet Content .....4
3.2. Selection Process ..........................................5
3.3. Reporting ..................................................7
3.4. Metering Process ...........................................7
3.5. Exporting Process ..........................................8
3.6. PSAMP Device ...............................................8
3.7. Collector ..................................................8
3.8. Selection Methods ..........................................8
4. Categorization of Packet Selection Techniques ..................11
5. Sampling .......................................................12
5.1. Systematic Sampling .......................................13
5.2. Random Sampling ...........................................14
5.2.1. n-out-of-N Sampling ................................14
5.2.2. Probabilistic Sampling .............................14
6. Filtering ......................................................16
6.1. Property Match Filtering ..................................16
6.2. Hash-Based Filtering ......................................19
6.2.1. Application Examples for Coordinated Packet
Selection ..........................................19
6.2.2. Desired Properties of Hash Functions ...............21
6.2.3. Security Considerations for Hash Functions .........22
6.2.4. Choice of Hash Function ............................26
7. Parameters for the Description of Selection Techniques .........29
7.1. Description of Sampling Techniques ........................30
7.2. Description of Filtering Techniques .......................31
8. Composite Techniques ...........................................34
8.1. Cascaded Filtering->Sampling or Sampling->Filtering .......34
8.2. Stratified Sampling .......................................34
9. Security Considerations ........................................35
10. Contributors ..................................................36
11. Acknowledgments ...............................................36
12. References ....................................................36
12.1. Normative References .....................................36
12.2. Informative References ...................................36
Appendix A. Hash Functions ........................................40
A.1 IP Shift-XOR (IPSX) Hash Function..............................40
A.2 BOB Hash Function..............................................41
1. Introduction
There are two main drivers for the evolution in measurement
infrastructures and their underlying technology. First, network data
rates are increasing, with a concomitant growth in measurement data.
Second, the growth is compounded by the demand of measurement-based
applications for increasingly fine-grained traffic measurements.
Devices that perform the measurements, require increasingly
sophisticated and resource-intensive measurement capabilities,
including the capture of packet headers or even parts of the payload,
and classification for flow analysis. All these factors can lead to
an overwhelming amount of measurement data, resulting in high demands
on resources for measurement, storage, transfer, and post processing.
The sustained capture of network traffic at line rate can be
performed by specialized measurement hardware. However, the cost of
the hardware and the measurement infrastructure required to
accommodate the measurements preclude this as a ubiquitous approach.
Instead, some form of data reduction at the point of measurement is
necessary.
This can be achieved by an intelligent packet selection through
Sampling or Filtering. Another way to reduce the amount of data is
to use aggregation techniques (not addressed in this document). The
motivation for Sampling is to select a representative subset of
packets that allow accurate estimates of properties of the unsampled
traffic to be formed. The motivation for Filtering is to remove all
packets that are not of interest. Aggregation combines data and
allows compact pre-defined views of the traffic. Examples of
applications that benefit from packet selection are given in
[RFC5474]. Aggregation techniques are out of scope of this document.
1.1. Conventions Used in This Document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. PSAMP Documents Overview
This document is one out of a series of documents from the PSAMP
group.
[RFC5474]: "A Framework for Packet Selection and Reporting" describes
the PSAMP framework for network elements to select subsets of packets
by statistical and other methods, and to export a stream of reports
on the selected packets to a Collector.
RFC 5475 (this document): "Sampling and Filtering Techniques for IP
Packet Selection" describes the set of packet selection techniques
supported by PSAMP.
[RFC5476]: "Packet Sampling (PSAMP) Protocol Specifications"
specifies the export of packet information from a PSAMP Exporting
Process to a PSAMP Collecting Process.
[RFC5477]: "Information Model for Packet Sampling Exports" defines an
information and data model for PSAMP.
3. Terminology
The PSAMP terminology defined here is fully consistent with all terms
listed in [RFC5474] but includes additional terms required for the
description of packet selection methods. An architecture overview
and possible configurations of PSAMP elements can be found in
[RFC5474]. PSAMP terminology also aims at consistency with terms
used in [RFC3917]. The relationship between PSAMP and IPFIX terms is
described in [RFC5474].
In the PSAMP documents, all defined PSAMP terms are written
capitalized. This document uses the same convention.
3.1. Observation Points, Packet Streams, and Packet Content
* Observation Point
An Observation Point [RFC5101] is a location in the network where
packets can be observed. Examples include:
(i) A line to which a probe is attached;
(ii) a shared medium, such as an Ethernet-based LAN;
(iii) a single port of a router, or set of interfaces (physical
or logical) of a router;
(iv) an embedded measurement subsystem within an interface.
Note that one Observation Point may be a superset of several other
Observation Points. For example, one Observation Point can be an
entire line card. This would be the superset of the individual
Observation Points at the line card's interfaces.
* Observed Packet Stream
The Observed Packet Stream is the set of all packets observed at
the Observation Point.
* Packet Stream
A Packet Stream denotes a set of packets from the Observed Packet
Stream that flows past some specified point within the Metering
Process. An example of a Packet Stream is the output of the
selection process. Note that packets selected from a stream,
e.g., by Sampling, do not necessarily possess a property by which
they can be distinguished from packets that have not been
selected. For this reason, the term "stream" is favored over
"flow", which is defined as a set of packets with common
properties [RFC3917].
* Packet Content
The Packet Content denotes the union of the packet header (which
includes link layer, network layer, and other encapsulation
headers) and the packet payload. At some Observation Points, the
link header information may not be available.
3.2. Selection Process
* Selection Process
A Selection Process takes the Observed Packet Stream as its input
and selects a subset of that stream as its output.
* Selection State
A Selection Process may maintain state information for use by the
Selection Process. At a given time, the Selection State may
depend on packets observed at and before that time, and other
variables. Examples include:
(i) sequence numbers of packets at the input of Selectors;
(ii) a timestamp of observation of the packet at the Observation
Point;
(iii) iterators for pseudorandom number generators;
(iv) hash values calculated during selection;
(v) indicators of whether the packet was selected by a given
Selector.
Selection Processes may change portions of the Selection State as
a result of processing a packet. Selection State for a packet is
to reflect the state after processing the packet.
* Selector
A Selector defines what kind of action a Selection Process
performs on a single packet of its input. If selected, the packet
becomes an element of the output Packet Stream.
The Selector can make use of the following information in
determining whether a packet is selected:
(i) the Packet Content;
(ii) information derived from the packet's treatment at the
Observation Point;
(iii) any Selection State that may be maintained by the Selection
Process.
* Composite Selector
A Composite Selector is an ordered composition of Selectors, in
which the output Packet Stream issuing from one Selector forms the
input Packet Stream to the succeeding Selector.
* Primitive Selector
A Selector is primitive if it is not a Composite Selector.
* Selection Sequence
From all the packets observed at an Observation Point, only a few
packets are selected by one or more Selectors. The Selection
Sequence is a unique value per Observation Domain describing the
Observation Point and the Selector IDs through which the packets
are selected.
3.3. Reporting
* Packet Reports
Packet Reports comprise a configurable subset of a packet's input
to the Selection Process, including the Packet's Content,
information relating to its treatment (for example, the output
interface), and its associated Selection State (for example, a
hash of the Packet's Content).
* Report Interpretation
Report Interpretation comprises subsidiary information, relating
to one or more packets, that is used for interpretation of their
Packet Reports. Examples include configuration parameters of the
Selection Process.
* Report Stream
The Report Stream is the output of a Metering Process, comprising
two distinguished types of information: Packet Reports and Report
Interpretation.
3.4. Metering Process
A Metering Process selects packets from the Observed Packet Stream
using a Selection Process, and produces as output a Report Stream
concerning the selected packets.
The PSAMP Metering Process can be viewed as analogous to the IPFIX
Metering Process [RFC5101], which produces Flow Records as its
output, with the difference that the PSAMP Metering Process always
contains a Selection Process. The relationship between PSAMP and
IPFIX is further described in [RFC5477] and [RFC5474].
3.5. Exporting Process
* Exporting Process
An Exporting Process sends, in the form of Export Packets, the
output of one or more Metering Processes to one or more
Collectors.
* Export Packet
An Export Packet is a combination of Report Interpretations and/or
one or more Packet Reports that are bundled by the Exporting
Process into an Export Packet for exporting to a Collector.
3.6. PSAMP Device
* PSAMP Device
A PSAMP Device is a device hosting at least an Observation Point,
a Metering Process (which includes a Selection Process), and an
Exporting Process. Typically, corresponding Observation Point(s),
Metering Process(es), and Exporting Process(es) are colocated at
this device, for example, at a router.
3.7. Collector
* Collector
A Collector receives a Report Stream exported by one or more
Exporting Processes. In some cases, the host of the Metering
and/or Exporting Processes may also serve as the Collector.
3.8. Selection Methods
* Filtering
A filter is a Selector that selects a packet deterministically
based on the Packet Content, or its treatment, or functions of
these occurring in the Selection State. Two examples are:
(i) Property Match Filtering: A packet is selected if a
specific field in the packet equals a predefined value.
(ii) Hash-based Selection: A Hash Function is applied to the
Packet Content, and the packet is selected if the result
falls in a specified range.
* Sampling
A Selector that is not a filter is called a Sampling operation.
This reflects the intuitive notion that if the selection of a
packet cannot be determined from its content alone, there must be
some type of Sampling taking place. Sampling operations can be
divided into two subtypes:
(i) Content-independent Sampling, which does not use Packet
Content in reaching Sampling decisions. Examples include
systematic Sampling, and uniform pseudorandom Sampling
driven by a pseudorandom number whose generation is
independent of Packet Content. Note that in content-
independent Sampling, it is not necessary to access the
Packet Content in order to make the selection decision.
(ii) Content-dependent Sampling, in which the Packet Content is
used in reaching selection decisions. An application is
pseudorandom selection according to a probability that
depends on the contents of a packet field, e.g., Sampling
packets with a probability dependent on their TCP/UDP port
numbers. Note that this is not a Filter.
* Hash Domain
A Hash Domain is a subset of the Packet Content and the packet
treatment, viewed as an N-bit string for some positive integer N.
* Hash Range
A Hash Range is a set of M-bit strings for some positive integer M
that defines the range of values that the result of the hash
operation can take.
* Hash Function
A Hash Function defines a deterministic mapping from the Hash
Domain into the Hash Range.
* Hash Selection Range
A Hash Selection Range is a subset of the Hash Range. The packet
is selected if the action of the Hash Function on the Hash Domain
for the packet yields a result in the Hash Selection Range.
* Hash-based Selection
A Hash-based Selection is Filtering specified by a Hash Domain, a
Hash Function, a Hash Range, and a Hash Selection Range.
* Approximative Selection
Selectors in any of the above categories may be approximated by
operations in the same or another category for the purposes of
implementation. For example, uniform pseudorandom Sampling may be
approximated by Hash-based Selection, using a suitable Hash
Function and Hash Domain. In this case, the closeness of the
approximation depends on the choice of Hash Function and Hash
Domain.
* Population
A Population is a Packet Stream or a subset of a Packet Stream. A
Population can be considered as a base set from which packets are
selected. An example is all packets in the Observed Packet Stream
that are observed within some specified time interval.
* Population Size
The Population Size is the number of all packets in the
Population.
* Sample Size
The Sample Size is a number of packets selected from the
Population by a Selector.
* Configured Selection Fraction
The Configured Selection Fraction is the expected ratio of the
Sample Size to the Population Size, as based on the configured
selection parameters.
* Attained Selection Fraction
The Attained Selection Fraction is the ratio of the actual Sample
Size to the Population Size. For some Sampling methods, the
Attained Selection Fraction can differ from the Configured
Selection Fraction due to, for example, the inherent statistical
variability in Sampling decisions of probabilistic Sampling and
Hash-based Selection. Nevertheless, for large Population Sizes
and properly configured Selectors, the Attained Selection Fraction
usually approaches the Configured Selection Fraction.
4. Categorization of Packet Selection Techniques
Packet selection techniques generate a subset of packets from an
Observed Packet Stream at an Observation Point. We distinguish
between Sampling and Filtering.
Sampling is targeted at the selection of a representative subset of
packets. The subset is used to infer knowledge about the whole set
of observed packets without processing them all. The selection can
depend on packet position, and/or on Packet Content, and/or on
(pseudo) random decisions.
Filtering selects a subset with common properties. This is used if
only a subset of packets is of interest. The properties can be
directly derived from the Packet Content, or depend on the treatment
given by the router to the packet. Filtering is a deterministic
operation. It depends on Packet Content or router treatment. It
never depends on packet position or on (pseudo) random decisions.
Note that a common technique to select packets is to compute a Hash
Function on some bits of the packet header and/or content and to
select it if the hash value falls in the Hash Selection Range. Since
hashing is a deterministic operation on the Packet Content, it is a
Filtering technique according to our categorization. Nevertheless,
Hash Functions are sometimes used to emulate random Sampling.
Depending on the chosen input bits, the Hash Function, and the Hash
Selection Range, this technique can be used to emulate the random
selection of packets with a given probability p. It is also a
powerful technique to consistently select the same packet subset at
multiple Observation Points [DuGr00].
The following table gives an overview of the schemes described in
this document and their categorization. X means that the
characteristic applies to the selection scheme. (X) denotes schemes
for which content-dependent and content-independent variants exist.
For instance, Property Match Filtering is typically based on Packet
Content and therefore is content dependent. But as explained in
Section 6.1, it may also depend on router state and then would be
independent of the content. It easily can be seen that only schemes
with both properties, content dependence and deterministic selection,
are considered as Filters.
Selection Scheme | Deterministic | Content -| Category
| Selection | Dependent|
------------------------+---------------+----------+----------
Systematic | X | _ | Sampling
Count-based | | |
------------------------+---------------+----------+----------
Systematic | X | - | Sampling
Time-based | | |
------------------------+---------------+----------+----------
Random | - | - | Sampling
n-out-of-N | | |
------------------------+---------------+----------+----------
Random | - | - | Sampling
uniform probabilistic | | |
------------------------+---------------+----------+----------
Random | - | (X) | Sampling
non-uniform probabil. | | |
------------------------+---------------+----------+----------
Random | - | (X) | Sampling
non-uniform Flow-State | | |
------------------------+---------------+----------+----------
Property Match | X | (X) | Filtering
Filtering | | |
------------------------+---------------+----------+----------
Hash function | X | X | Filtering
------------------------+---------------+----------+----------
The categorization just introduced is mainly useful for the
definition of an information model describing Primitive Selectors.
More complex selection techniques can be described through the
composition of cascaded Sampling and Filtering operations. For
example, a packet selection that weights the selection probability on
the basis of the packet length can be described as a cascade of a
Filtering and a Sampling scheme. However, this descriptive approach
is not intended to be rigid: if a common and consolidated selection
practice turns out to be too complex to be described as a composition
of the mentioned building blocks, an ad hoc description can be
specified instead and added as a new scheme to the information model.
5. Sampling
The deployment of Sampling techniques aims at the provisioning of
information about a specific characteristic of the parent Population
at a lower cost than a full census would demand. In order to plan a
suitable Sampling strategy, it is therefore crucial to determine the
needed type of information and the desired degree of accuracy in
advance.
First of all, it is important to know the type of metric that should
be estimated. The metric of interest can range from simple packet
counts [JePP92] up to the estimation of whole distributions of flow
characteristics (e.g., packet sizes) [ClPB93].
Second, the required accuracy of the information and with this, the
confidence that is aimed at, should be known in advance. For
instance, for usage-based accounting the required confidence for the
estimation of packet counters can depend on the monetary value that
corresponds to the transfer of one packet. That means that a higher
confidence could be required for expensive packet flows (e.g.,
premium IP service) than for cheaper flows (e.g., best effort). The
accuracy requirements for validating a previously agreed quality can
also vary extremely with the customer demands. These requirements
are usually determined by the service level agreement (SLA).
The Sampling method and the parameters in use must be clearly
communicated to all applications that use the measurement data. Only
with this knowledge a correct interpretation of the measurement
results can be ensured.
Sampling methods can be characterized by the Sampling algorithm, the
trigger type used for starting a Sampling interval, and the length of
the Sampling interval. These parameters are described here in
detail. The Sampling algorithm describes the basic process for
selection of samples. In accordance to [AmCa89] and [ClPB93], we
define the following basic Sampling processes.
5.1. Systematic Sampling
Systematic Sampling describes the process of selecting the start
points and the duration of the selection intervals according to a
deterministic function. This can be for instance the periodic
selection of every k-th element of a trace but also the selection of
all packets that arrive at predefined points in time. Even if the
selection process does not follow a periodic function (e.g., if the
time between the Sampling intervals varies over time), we consider
this as systematic Sampling as long as the selection is
deterministic.
The use of systematic Sampling always involves the risk of biasing
the results. If the systematics in the Sampling process resemble
systematics in the observed stochastic process (occurrence of the
characteristic of interest in the network), there is a high
probability that the estimation will be biased. Systematics in the
observed process might not be known in advance.
Here only equally spaced schemes are considered, where triggers for
Sampling are periodic, either in time or in packet count. All
packets occurring in a selection interval (either in time or packet
count) beyond the trigger are selected.
Systematic count-based
In systematic count-based Sampling, the start and stop triggers for
the Sampling interval are defined in accordance to the spatial packet
position (packet count).
Systematic time-based
In systematic time-based Sampling, time-based start and stop triggers
are used to define the Sampling intervals. All packets are selected
that arrive at the Observation Point within the time intervals
defined by the start and stop triggers (i.e., arrival time of the
packet is larger than the start time and smaller than the stop time).
Both schemes are content-independent selection schemes. Content-
dependent deterministic Selectors are categorized as filters.
5.2. Random Sampling
Random Sampling selects the starting points of the Sampling intervals
in accordance to a random process. The selection of elements is an
independent experiment. With this, unbiased estimations can be
achieved. In contrast to systematic Sampling, random Sampling
requires the generation of random numbers. One can differentiate two
methods of random Sampling: n-out-of-N Sampling and probabilistic
Sampling.
5.2.1. n-out-of-N Sampling
In n-out-of-N Sampling, n elements are selected out of the parent
Population that consists of N elements. One example would be to
generate n different random numbers in the range [1,N] and select all
packets that have a packet position equal to one of the random
numbers. For this kind of Sampling, the Sample Size n is fixed.
5.2.2. Probabilistic Sampling
In probabilistic Sampling, the decision whether or not an element is
selected is made in accordance to a predefined selection probability.
An example would be to flip a coin for each packet and select all
packets for which the coin showed the head. For this kind of
Sampling, the Sample Size can vary for different trials. The
selection probability does not necessarily have to be the same for
each packet. Therefore, we distinguish between uniform probabilistic
Sampling (with the same selection probability for all packets) and
non-uniform probabilistic Sampling (where the selection probability
can vary for different packets).
5.2.2.1. Uniform Probabilistic Sampling
For Uniform Probabilistic Sampling, packets are selected
independently with a uniform probability p. This Sampling can be
count-driven, and is sometimes referred to as geometric random
Sampling, since the difference in count between successive selected
packets is an independent random variable with a geometric
distribution of mean 1/p. A time-driven analog, exponential random
Sampling, has the time between triggers exponentially distributed.
Both geometric and exponential random Sampling are examples of what
is known as additive random Sampling, defined as Sampling where the
intervals or counts between successive samples are independent
identically distributed random variables.
5.2.2.2. Non-Uniform Probabilistic Sampling
This is a variant of Probabilistic Sampling in which the Sampling
probabilities can depend on the selection process input. This can be
used to weight Sampling probabilities in order, e.g., to boost the
chance of Sampling packets that are rare but are deemed important.
Unbiased estimators for quantitative statistics are recovered by
re-normalization of sample values; see [HT52].
5.2.2.3. Non-Uniform Flow State Dependent Sampling
Another type of Sampling that can be classified as probabilistic
Non-Uniform is closely related to the flow concept as defined in
[RFC3917], and it is only used jointly with a flow monitoring
function (IPFIX Metering Process). Packets are selected, dependent
on a Selection State. The point, here, is that the Selection State
is determined also by the state of the flow the packet belongs to
and/or by the state of the other flows currently being monitored by
the associated flow monitoring function. An example for such an
algorithm is the "sample and hold" method described in [EsVa01]:
- If a packet accounts for a Flow Record that already exists in the
IPFIX flow recording process, it is selected (i.e., the Flow Record
is updated).
- If a packet doesn't account for any existing Flow Record, it is
selected with probability p. If it has been selected, a new Flow
Record has to be created.
A further algorithm that fits into the category of non-uniform flow
state dependent Sampling is described in [Moli03].
This type of Sampling is content dependent because the identification
of the flow the packet belongs to requires analyzing part of the
Packet Content. If the packet is selected, then it is passed as an
input to the IPFIX monitoring function (this is called "Local Export"
in [RFC5474]). Selecting the packet depending on the state of a flow
cache is useful when memory resources of the flow monitoring function
are scarce (i.e., there is no room to keep all the flows that have
been scheduled for monitoring).
5.2.2.4. Configuration of Non-Uniform Probabilistic and Flow State
Sampling
Many different specific methods can be grouped under the terms
non-uniform probabilistic and flow state Sampling. Dependent on the
Sampling goal and the implemented scheme, a different number and type
of input parameters are required to configure such a scheme.
Some concrete proposals for such methods exist from the research
community (e.g., [EsVa01], [DuLT01], [Moli03]). Some of these
proposals are still in an early stage and need further investigations
to prove their usefulness and applicability. It is not our aim to
indicate preference among these methods. Instead, we only describe
here the basic methods and leave the specification of explicit
schemes and their parameters up to vendors (e.g., as an extension of
the information model).
6. Filtering
Filtering is the deterministic selection of packets based on the
Packet Content, the treatment of the packet at the Observation Point,
or deterministic functions of these occurring in the Selection State.
The packet is selected if these quantities fall into a specified
range. The role of Filtering, as the word itself suggest, is to
separate all the packets having a certain property from those not
having it. A distinguishing characteristic from Sampling is that the
selection decision does not depend on the packet position in time or
in space, or on a random process.
We identify and describe in the following two Filtering techniques.
6.1. Property Match Filtering
With this Filtering method, a packet is selected if specific fields
within the packet and/or properties of the router state equal a
predefined value. Possible filter fields are all IPFIX flow
attributes specified in [RFC5102]. Further fields can be defined by
proposing new information elements or defining vendor-specific
extensions.
A packet is selected if Field=Value. Masks and ranges are only
supported to the extent to which [RFC5102] allows them, e.g., by
providing explicit fields like the netmasks for source and
destination addresses.
AND operations are possible by concatenating filters, thus producing
a composite selection operation. In this case, the ordering in which
the Filtering happens is implicitly defined (outer filters come after
inner filters). However, as long as the concatenation is on filters
only, the result of the cascaded filter is independent from the
order, but the order may be important for implementation purposes, as
the first filter will have to work at a higher rate. In any case, an
implementation is not constrained to respect the filter ordering, as
long as the result is the same, and it may even implement the
composite Filtering in one single step.
OR operations are not supported with this basic model. More
sophisticated filters (e.g., supporting bitmasks, ranges, or OR
operations) can be realized as vendor-specific schemes.
All IPFIX flow attributes defined in [RFC5102] can be used for
Property Match Filtering. Further information elements can be easily
defined. Property match operations should be available for different
protocol portions of the packet header:
(i) IP header (excluding options in IPv4, stacked headers in
IPv6)
(ii) transport protocol header (e.g., TCP, UDP)
(iii) encapsulation headers (e.g., the MPLS label stack, if
present)
When the PSAMP Device offers Property Match Filtering, and, in its
usual capacity other than in performing PSAMP functions, identifies
or processes information from IP, transport protocol or encapsulation
protocols, then the information should be made available for
Filtering. For example, when a PSAMP Device routes based on
destination IP address, that field should be made available for
Filtering. Conversely, a PSAMP Device that does not route is not
expected to be able to locate an IP address within a packet, or make
it available for Filtering, although it may do so.
Since packet encryption conceals the real values of encrypted fields,
Property Match Filtering must be configurable to ignore encrypted
packets, when detected.
The Selection Process may support Filtering based on the properties
of the router state:
(i) Ingress interface at which a packet arrives equals a
specified value
(ii) Egress interface to which a packet is routed to equals a
specified value
(iii) Packet violated Access Control List (ACL) on the router
(iv) Failed Reverse Path Forwarding (RPF)
(v) Failed Resource Reservation Protocol (RSVP)
(vi) No route found for the packet
(vii) Origin Border Gateway Protocol (BGP) Autonomous System (AS)
[RFC4271] equals a specified value or lies within a given
range
(viii) Destination BGP AS equals a specified value or lies within
a given range
Packets that match the failed Reverse Path Forwarding (RPF) condition
are packets for which ingress Filtering failed as defined in
[RFC3704].
Packets that match the failed Resource Reservation Protocol (RSVP)
condition are packets that do not fulfill the RSVP specification as
defined in [RFC2205].
Router architectural considerations may preclude some information
concerning the packet treatment being available at line rate for
selection of packets. For example, the Selection Process may not be
implemented in the fast path that is able to access router state at
line rate. However, when Filtering follows Sampling (or some other
selection operation) in a Composite Selector, the rate of the Packet
Stream output from the sampler and input to the filter may be
sufficiently slow that the filter could select based on router state.
6.2. Hash-Based Filtering
A Hash Function h maps the Packet Content c, or some portion of it,
onto a Hash Range R. The packet is selected if h(c) is an element of
S, which is a subset of R called the Hash Selection Range. Thus,
Hash-Based selection is a particular case of Filtering. The object
is selected if c is in inv(h(S)). But for desirable Hash Functions,
the inverse image inv(h(S)) will be extremely complex, and hence h
would not be expressible as, say, a Property Match Filter or a simple
combination of these.
Hash-based Selection is mainly used to realize a coordinated packet
selection. That means that the same packets are selected at
different Observation Points. This is useful for instance to observe
the path (trajectory) that a packet took through the network or to
apply packet selection to passive one-way measurements.
A prerequisite for the method to work and to ensure interoperability
is that the same Hash Function with the same parameters (e.g., input
vector) is used at the Observation Points.
A consistent packet selection is also possible with Property Match
Filtering. Nevertheless, Hash-based Selection can be used to
approximate a random selection. The desired statistical properties
are discussed in Section 6.2.2.
In the following subsections, we give some application examples for
coordinated packet selection.
6.2.1. Application Examples for Coordinated Packet Selection
6.2.1.1. Trajectory Sampling
Trajectory Sampling is the consistent selection of a subset of
packets at either all of a set of Observation Points or none of them.
Trajectory Sampling is realized by Hash-based Selection if all
Observation Points in the set use a common Hash Function, Hash
Domain, and Selection Range. The Hash Domain comprises all or part
of the Packet Content that is invariant along the packet path.
Fields such as Time-to-Live, which is decremented per hop, and header
CRC [RFC1624], which is recalculated per hop, are thus excluded from
the Hash Domain. The Hash Domain needs to be wider than just a flow
key, if packets are to be selected quasi-randomly within flows.
The trajectory (or path) followed by a packet is reconstructed from
PSAMP reports on it that reach a Collector. Reports on a given
packet originating from different Observation Points are associated
by matching a label from the reports. The label may comprise that
portion of the invariant Packet Content that is reported, or possibly
some digest of the invariant Packet Content that is inserted into the
packet report at the Observation Point. Such a digest may be
constructed by applying a second Hash Function to the invariant
Packet Content. The reconstruction of trajectories and methods for
dealing with possible ambiguities due to label collisions (identical
labels reported for different packets) and potential loss of reports
in transmission are dealt with in [DuGr00], [DuGG02], and [DuGr04].
Applications of trajectory Sampling include (i) estimation of the
network path matrix, i.e., the traffic intensities according to
network path, broken down by flow key; (ii) detection of routing
loops, as indicated by self-intersecting trajectories; (iii) passive
performance measurement: prematurely terminating trajectories
indicate packet loss, packet one-way delay can be determined if
reports include (synchronized) timestamps of packet arrival at the
Observation Point; and (iv) network attack tracing, of the actual
paths taken by attack packets with spoofed source addresses.
6.2.1.2. Passive One-Way Measurements
Coordinated packet selection can be applied for instance to one-way
delay measurements in order to reduce the required resources. In
one-way delay measurements, packets are collected at different
Observation Points in the network. A packet digest is generated for
each packet that helps to identify the packet. The packet digest and
the arrival time of the packet at the Observation Point are reported
to a process that calculates the delay. The delay is calculated by
subtracting the arrival time of the same packet at the Observation
Points (e.g., [ZsZC01]). With high data rates, capturing all packets
can require a lot of resources for storage, transfer, and processing.
To reduce resource consumption, packet selection methods can be
applied. But for such selection techniques, it has to be ensured
that the same packets are collected at different Observation Points.
Hash-based Selection provides this feature.
6.2.1.3. Generation of Pseudorandom Numbers
Although pseudorandom number generators with well-understood
properties have been developed, they may not be the method of choice
in settings where computational resources are scarce. A convenient
alternative is to use Hash Functions of Packet Content as a source of
randomness. The hash (suitably re-normalized) is a pseudorandom
variate in the interval [0,1]. Other schemes may use packet fields
in iterators for pseudorandom numbers. However, the statistical
properties of an ideal packet selection law (such as independent
Sampling for different packets, or independence on Packet Content)
may not be exactly rendered by an implementation, but only
approximately so.
Use of Packet Content to generate pseudorandom variates shares with
non-uniform probabilistic Sampling (see Section 5.2.2.2 above) the
property that selection decisions depend on Packet Content. However,
there is a fundamental difference between the two. In the former
case, the content determines pseudorandom variates. In the latter
case, the content only determines the selection probabilities:
selection could then proceed, e.g., by use of random variates
obtained by an independent pseudorandom number generator.
6.2.2. Desired Properties of Hash Functions
Here we formulate desired properties for Hash Functions. For this,
we have to distinguish whether a Hash Function is used for packet
selection or just as a packet digest. The main focus of this
document is on packet selection. Nevertheless, we also provide some
requirements for the use of Hash Functions as packet digest.
First of all, we need to define suitable input fields from the
packet. In accordance to [DuGr00], input field should be:
- invariant on the path
- variable among packets
Only if the input fields are the same at different Observation Points
is it possible to recognize the packet. The input fields should be
variable among packets in order to distribute the hash results over
the selection range.
6.2.2.1. Requirements for Packet Selection
In accordance to considerations in [MoND05] and [Henk08], we define
the following desired properties of Hash Functions used for packet
selection:
(i) Speed: The Hash Function has to be applied to each packet
that traverses the Observation Point. Therefore, it has to
be fast in order to cope with the high packet rates. In
the ideal case, the hash operation should not influence the
performance on the PSAMP Device.
(ii) Uniformity: The Hash Function h should have good mixing
properties, in the sense that small changes in the input
(e.g., the flipping of a single bit) cause large changes in
the output (many bits change). Then any local clump of
values of c is spread widely over R by h, and so the
distribution of h(c) is fairly uniform even if the
distribution of c is not. Then the Attained Selection
Fraction gets close to the Configured Selection Fraction
(#S/#R), which can be tuned by choice of S.
(iii) Unbiasedness: The selection decision should be as
independent of packet attributes as possible. The set of
selected packets should not be biased towards a specific
type of packets.
(iv) Representativeness of sample: The sample should be as
representative as possible for the observed traffic.
(v) Non-linearity: The function should not be linear. This
increases the mixing properties (uniformity criterion). In
addition to this, it decreases the predictability of the
output and therefore the vulnerabilities against attacks.
(vi) Robustness against vulnerabilities: The Hash Function
should be robust against attacks. Potential
vulnerabilities are described in Section 6.2.3.
6.2.2.2. Requirements for Packet Digesting
For digesting Packet Content for inclusion in a reported label, the
most important property is a low collision frequency. A secondary
requirement is the ability to accept variable-length input, in order
to allow inclusion of maximal amount of packet as input. Execution
speed is of secondary importance, since the digest need only be
formed from selected packets.
6.2.3. Security Considerations for Hash Functions
A concern for Hash-based Selection is whether some large set of
related packets could be disproportionately sampled, i.e., that the
Attained Selection Fraction is significantly different from the
Configured Selection Fraction. This can happen either
(i) through unanticipated behavior in the Hash Function, or
(ii) because the packets had been deliberately crafted to have
this property.
The first point underlines the importance of using a Hash Function
with good mixing properties. For this, the statistical properties of
candidate Hash Functions need to be evaluated. Since the hash output
depends on the traffic mix, the evaluation should be done preferably
on up-to-date packet traces from the network in which the Hash-based
Selection will be deployed.
However, Hash Functions that perform well on typical traffic may not
be sufficiently strong to withstand attacks specifically targeted
against them. Such potential attacks have been described in
[GoRe07].
In the following subsections, we point out different potential attack
scenarios. We encourage the use of standardized Hash Functions.
Therefore, we assume that the Hash Function itself is public and
hence known to an attacker.
Nevertheless, we also assume the possibility of using a private input
parameter for the Hash Function that is kept secret. Such an input
parameter can for instance be attached to the hash input before the
hash operation is applied. With this, at least parts of the hash
operation remain secret.
For the attack scenarios, we assume that an attacker uses its
knowledge of the Hash Function to craft packets that are then
dispatched, either as the attack itself or to elicit further
information that can be used to refine the attack.
Two scenarios are considered. In the first scenario, the attacker
has no knowledge about whether or not the crafted packets are
selected. In the second scenario, the attacker uses some knowledge
of Sampling outcomes. The means by which this might be acquired is
discussed below. Some additional attacks that involve tampering with
Export Packets in transit, as opposed to attacking the PSAMP Device,
are discussed in [GoRe07].
6.2.3.1. Vulnerabilities of Hash-Based Selection without Knowledge of
Selection Outcomes
(i) The Hash Function does not use a private parameter.
If no private input parameter is used, potential attackers can easily
calculate which packets result in which hash values. If the
selection range is public, an attacker can craft packets whose
selection properties are known in advance. If the selection range is
private, an attacker cannot determine whether a crafted packet is
selected. However, by computing the hash on different trial crafted
packets, and selecting those yielding a given hash value, the
attacker can construct an arbitrarily large set of distinct packets
with a common selection properties, i.e., packets that will be either
all selected or all not selected. This can be done whatever the
strength of the Hash Function.
(ii) The Hash Function is not cryptographically strong.
If the Hash Function is not cryptographically strong, it may be
possible to construct sequences of distinct packets with the common
selection property even if a private parameter is used.
An example is the standard CRC-32 Hash Function used with a private
modulus (but without a private string post-pended to the input). It
has weak mixing properties for low-order bits. Consequently, simply
by incrementing the hash input, one obtains distinct packets whose
hashes mostly fall in a narrow range, and hence are likely commonly
selected; see [GoRe07].
Suitable parameterization of the Hash Function can make such attacks
more difficult. For example, post-pending a private string to the
input before hashing with CRC-32 will give stronger mixing properties
over all bits of the input. However, with a Hash Function, such as
CRC-32, that is not cryptographically strong, the possibility of
discovering a method to construct packet sets with the common
selected property cannot be ruled out, even when a private modulus or
post-pended string is used.
6.2.3.2. Vulnerabilities of Hash-Based Selection Using Knowledge of
Selection Outcomes
Knowledge of the selection outcomes of crafted packets can be used by
an attacker to more easily construct sets of packets that are
disproportionately sampled and/or are commonly selected. For this,
the attacker does not need any a priori knowledge about the Hash
Function or selection range.
There are several ways an attacker might acquire this knowledge about
the selection outcome:
(i) Billing Reports: If samples are used for billing purposes,
then the selection outcomes of packets may be able to be
inferred by correlating a crafted Packet Stream with the
billing reports that it generates. However, the rate at
which knowledge of selection outcomes can be acquired
depends on the temporal and spatial granularity of the
billing reports; being slower the more aggregated the
reports are.
(ii) Feedback from an Intrusion Detection System: e.g., a
botmaster adversary learns if his packets were detected by
the intrusion detection system by seeing if one of his bots
is blocked by the network.
(iii) Observation of the Report Stream: Export Packets sent
across a public network may be eavesdropped on by an
adversary. Encryption of the Export Packets provides only
a partial defense, since it may be possible to infer the
selection outcomes of packets by correlating a crafted
Packet Stream with the occurrence (not the content) of
packets in the export stream that it generates. The rate
at which such knowledge could be acquired is limited by the
temporal resolution at which reports can be associated with
packets, e.g., due to processing and propagation
variability, and difficulty in distinguishing report on
attack packets from those of background traffic, if
present. The association between packets and their reports
on which this depends could be removed by padding Export
Packets to a constant length and sending them at a constant
rate.
We now turn to attacks that can exploit knowledge of selection
outcomes. First, with a non-cryptographic Hash Function, knowledge
of selection outcomes for a trial stream may be used to further craft
a packet set with the common selection property. This has been
demonstrated for the modular hash f(x) = a x + b mod k, for private
parameters a, b, and k. With Sampling rate p, knowledge of the
Sampling outcomes of roughly 2/p is sufficient for the attack to
succeed, independent of the values of a, b, and k. With knowledge of
the selection outcomes of a larger number of packets, the parameters
a, b, and k can be determined; see [GoRe07].
A cryptographic Hash Function employing a private parameter and
operating in one of the pseudorandom function modes specified above
is not vulnerable to these attacks, even if the selection range is
known.
6.2.3.3. Vulnerabilities to Replay Attacks
Since Hash-based Selection is deterministic, any packet or set of
packets with known selection properties can be replayed into a
network and experience the same selection outcomes provide the Hash
Function and its parameters are not changed. Repetition of a single
packet may be noticeable to other measurement methods if employed
(e.g., collection of flow statistics), whereas a set of distinct
packets that appears statistically similar to regular traffic may be
less noticeable.
Replay attacks may be mitigated by repeated changing of Hash Function
parameters. This also prevents attacks that exploit knowledge of
Sampling outcomes, at least if the parameters are changed at least as
fast as the knowledge can be acquired by an attacker. In order to
preserve the ability to perform trajectory Sampling, parameter change
would have to be simultaneous (or approximately so) across all
Observation Points.
6.2.4. Choice of Hash Function
The specific choice of Hash Function represents a trade-off between
complexity and ease of implementation. Ideally, a cryptographically
strong Hash Function employing a private parameter and operating in
pseudorandom function mode as specified above would be used, yielding
a good emulation of a random packet selection at a target Sampling
rate, and giving maximal robustness against the attacks described in
the previous section. Unfortunately, there is currently no single
Hash Function that fulfills all the requirements.
As detailed in Section 6.2.3, only cryptographic Hash Functions
employing a private parameter operating in pseudorandom function mode
are sufficiently strong to withstand the range of conceivable
attacks. For example, fixed- or variable-length inputs could be
hashed using a block cipher (like Advanced Encryption Standard (AES))
in cipher-block-chaining mode. Fixed-length inputs could also be
hashed using an iterated cryptographic Hash Function (like MD5 or
SHA1), with a private initial vector. For variable-length inputs, an
iterated cryptographic Hash Function (like MD5 or SHA1) should employ
private string post-pended to the data in addition to a private
initial vector. For more details, see the "append-cascade"
construction of [BeCK96]. We encourage the use of such
cryptographically strong Hash Functions wherever possible.
However, a problem with using such functions is the low performance.
As shown for instance in [Henk08], the computation times for MD5 and
SHA are about 7-10 times higher compared to non-cryptographic
functions. The difference increases for small hash input lengths.
Therefore, it is not assumed that all PSAMP Devices will be capable
of applying a cryptographically strong Hash Function to every packet
at line rate. For this reason, the Hash Functions listed in this
section will be of a weaker variety. Future protocol extensions that
employ stronger Hash Functions are highly welcome.
Comparisons of Hash Functions for packet selection and packet
digesting with regard to various criteria can be found in [MoND05]
and [Henk08].
6.2.4.1. Hash Functions for Packet Selection
If Hash-based packet Selection is applied, the BOB function MUST be
used for packet selection operations in order to be compliant with
PSAMP. The specification of BOB is given in the appendix. Both the
parameter (the init value) and the selection range should be kept
private. The initial vector of the Hash Function MUST be
configurable out of band to prevent security breaches like exposure
of the initial vector content.
Other functions, such as CRC-32 and IPSX, MAY be used. The IPSX
function is described in the appendix, and the CRC-32 function is
described in [RFC1141]. If CRC-32 is used, the input should first be
post-pended with a private string that acts as a parameter, and the
modulus of the CRC should also be kept private.
IPSX is simple to implement and was correspondingly about an order of
magnitude faster to execute per packet than BOB or CRC-32 [MoND05].
All three Hash Functions evaluated showed relatively poor uniformity
with 16-byte input that was drawn from only invariant fields in the
IP and TCP/UDP headers (i.e., header fields that do not change from
hop to hop). IPSX is inherently limited to 16 bytes.
BOB and CRC-32 exhibit noticeably better uniformity when 4 or more
bytes from the payload are also included in the input [MoND05]. Also
with other criteria BOB performed quite well [Henk08].
Although the characteristics have been checked for different traffic
traces, results cannot be generalized to arbitrary traffic. Since
Hash-based Selection is a deterministic function on the Packet
Content, it can always be biased towards packets with specific
attributes. Furthermore, it should be noted that all Hash Functions
were evaluated only for IPv4.
None of these Hash Functions is recommended for cryptographic
purposes. Please also note that the use of a private parameter only
slightly reduces the vulnerabilities against attacks. As shown in
Section 6.2.3, functions that are not cryptographically strong (e.g.,
BOB and CRC) cannot prevent attackers from crafting packets that are
disproportionally selected even if a private parameter is used and
the selection range is kept secret.
As described in Section 6.2.2, the input bytes for the Hash Function
need to be invariant along the path the packet is traveling. Only
with this it is ensured that the same packets are selected at
different Observation Points. Furthermore, they should have a high
variability between different packets to generate a high variation in
the Hash Range. An evaluation of the variability of different packet
header fields can be found in [DuGr00], [HeSZ08], and [Henk08].
If a Hash-based Selection with the BOB function is used with IPv4
traffic, the following input bytes MUST be used.
- IP identification field
- Flags field
- Fragment offset
- Source IP address
- Destination IP address
- A configurable number of bytes from the IP payload, starting at
a configurable offset
Due to the lack of suitable IPv6 packet traces, all candidate Hash
Functions in [DuGr00], [MoND05], and [Henk08] were evaluated only for
IPv4. Due to the IPv6 header fields and address structure, it is
expected that there is less randomness in IPv6 packet headers than in
IPv4 headers. Nevertheless, the randomness of IPv6 traffic has not
yet been evaluated sufficiently to get any evidence. In addition to
this, IPv6 traffic profiles may change significantly in the future
when IPv6 is used by a broader community.
If a Hash-based Selection with the BOB function is used with IPv6
traffic, the following input bytes MUST be used.
- Payload length (2 bytes)
- Byte number 10,11,14,15,16 of the IPv6 source address
- Byte number 10,11,14,15,16 of the IPv6 destination address
- A configurable number of bytes from the IP payload, starting at
a configurable offset. It is recommended to use at least 4
bytes from the IP payload.
The payload itself is not changing during the path. Even if some
routers process some extension headers, they are not going to strip
them from the packet. Therefore, the payload length is invariant
along the path. Furthermore, it usually differs for different
packets. The IPv6 address has 16 bytes. The first part is the
network part and contains low variation. The second part is the host
part and contains higher variation. Therefore, the second part of
the address is used. Nevertheless, the uniformity has not been
checked for IPv6 traffic.
6.2.4.2. Hash Functions Suitable for Packet Digesting
For this purpose also the BOB function SHOULD be used. Other
functions (such as CRC-32) MAY be used. Among the functions capable
of operating with variable-length input, BOB and CRC-32 have the
fastest execution, BOB being slightly faster. IPSX is not
recommended for digesting because it has a significantly higher
collision rate and takes only a fixed-length input.
7. Parameters for the Description of Selection Techniques
This section gives an overview of different alternative selection
schemes and their required parameters. In order to be compliant with
PSAMP, at least one of proposed schemes MUST be implemented.
The decision whether or not to select a packet is based on a function
that is performed when the packet arrives at the selection process.
Packet selection schemes differ in the input parameters for the
selection process and the functions they require to do the packet
selection. The following table gives an overview.
Scheme | Input parameters | Functions
---------------+------------------------+-------------------
systematic | packet position | packet counter
count-based | Sampling pattern |
---------------+------------------------+-------------------
systematic | arrival time | clock or timer
time-based | Sampling pattern |
---------------+------------------------+-------------------
random | packet position | packet counter,
n-out-of-N | Sampling pattern | random numbers
| (random number list) |
---------------+------------------------+-------------------
uniform | Sampling | random function
probabilistic | probability |
---------------+------------------------+-------------------
non-uniform |e.g., packet position, | selection function,
probabilistic | Packet Content(parts) | probability calc.
---------------+------------------------+-------------------
non-uniform |e.g., flow state, | selection function,
flow-state | Packet Content(parts) | probability calc.
---------------+------------------------+-------------------
property | Packet Content(parts) | filter function or
match | or router state | state discovery
---------------+------------------------+-------------------
hash-based | Packet Content(parts) | Hash Function
---------------+------------------------+-------------------
7.1. Description of Sampling Techniques
In this section, we define what elements are needed to describe the
most common Sampling techniques. Here the selection function is
predefined and given by the Selector ID.
Sampler Description:
SELECTOR_ID
SELECTOR_TYPE
SELECTOR_PARAMETERS
Where:
SELECTOR_ID:
Unique ID for the packet sampler.
SELECTOR_TYPE:
For Sampling processes, the SELECTOR TYPE defines what Sampling
algorithm is used.
Values: Systematic count-based | Systematic time-based | Random
|n-out-of-N | uniform probabilistic | Non-uniform probabilistic |
Non-uniform flow state
SELECTOR_PARAMETERS:
For Sampling processes, the SELECTOR PARAMETERS define the input
parameters for the process. Interval length in systematic Sampling
means that all packets that arrive in this interval are selected.
The spacing parameter defines the spacing in time or number of
packets between the end of one Sampling interval and the start of the
next succeeding interval.
Case n-out-of-N:
- Population Size N, Sample size n
Case systematic time-based:
- Interval length (in usec), Spacing (in usec)
Case systematic count-based:
- Interval length (in packets), Spacing (in packets)
Case uniform probabilistic (with equal probability per packet):
- Sampling probability p
Case non-uniform probabilistic:
- Calculation function for Sampling probability p (see also
Section 5.2.2.4)
Case flow state:
- Information reported for flow state Sampling is not defined in
this document (see also Section 5.2.2.4)
7.2. Description of Filtering Techniques
In this section, we define what elements are needed to describe the
most common Filtering techniques. The structure closely parallels
the one presented for the Sampling techniques.
Filter Description:
SELECTOR_ID
SELECTOR_TYPE
SELECTOR_PARAMETERS
Where:
SELECTOR_ID:
Unique ID for the packet filter. The ID can be calculated under
consideration of the SELECTION SEQUENCE and a local ID.
SELECTOR_TYPE:
For Filtering processes, the SELECTOR TYPE defines what Filtering
type is used.
Values: Matching | Hashing | Router_state
SELECTOR_PARAMETERS:
For Filtering processes, the SELECTOR PARAMETERS define formally the
common property of the packet being filtered. For the filters of
type matching and hashing, the definitions have a lot of points in
common.
Values:
Case matching:
- Information Element (from [RFC5102])
- Value (type in accordance to [RFC5102])
In case of multiple match criteria, multiple "case matching" has to
be bound by a logical AND.
Case hashing:
- Hash Domain (input bits from packet)
- <Header type = IPv4>
- <Input bit specification, header part>
- <Header type = IPv6>
- <Input bit specification, header part>
- <payload byte number N>
- <Input bit specification, payload part>
- Hash Function
- Hash Function name
- Length of input key (eliminate 0x bytes)
- Output value (length M and bitmask)
- Hash Selection Range, as a list of non-overlapping
intervals [start value, end value] where value is in
[0,2^M-1]
- Additional parameters are dependent on specific Hash
Function (e.g., hash input bits (seed))
Notes to input bits for case hashing:
- Input bits can be from header part only, from the payload part
only, or from both.
- The bit specification, for the header part, can be specified for
IPv4 or IPv6 only, or both.
- In case of IPv4, the bit specification is a sequence of 20
hexadecimal numbers [00,FF] specifying a 20-byte bitmask to be
applied to the header.
- In case of IPv6, it is a sequence of 40 hexadecimal numbers [00,FF]
specifying a 40-byte bitmask to be applied to the header.
- The bit specification, for the payload part, is a sequence of
hexadecimal numbers [00,FF] specifying the bitmask to be applied to
the first N bytes of the payload, as specified by the previous
field. In case the hexadecimal number sequence is longer than N,
only the first N numbers are considered.
- In case the payload is shorter than N, the Hash Function cannot be
applied. Other options, like padding with zeros, may be considered
in the future.
- A Hash Function cannot be defined on the options field of the IPv4
header, neither on stacked headers of IPv6.
- The Hash Selection Range defines a range of hash values (out of all
possible results of the hash operation). If the hash result for a
specific packet falls in this range, the packet is selected. If
the value is outside the range, the packet is not selected. For
example, if the selection interval specification is [1:3], [6:9]
all packets are selected for which the hash result is 1,2,3,6,7,8,
or 9. In all other cases, the packet is not selected.
Case router state:
- Ingress interface at which the packet arrives equals a specified
value
- Egress interface to which the packet is routed equals a specified
value
- Packet violated Access Control List (ACL) on the router
- Reverse Path Forwarding (RPF) failed for the packet
- Resource Reservation is insufficient for the packet
- No route is found for the packet
- Origin AS equals a specified value or lies within a given range
- Destination AS equals a specified value or lies within a given
range
Note to case router state:
- All router state entries can be linked by AND operators
8. Composite Techniques
Composite schemes are realized by combining the Selector IDs into a
Selection Sequence. The Selection Sequence contains all Selector IDs
that are applied to the Packet Stream subsequently. Some examples of
composite schemes are reported below.
8.1. Cascaded Filtering->Sampling or Sampling->Filtering
If a filter precedes a Sampling process, the role of Filtering is to
create a set of "parent populations" from a single stream that can
then be fed independently to different Sampling functions, with
different parameters tuned for the Population itself (e.g., if
streams of different intensity result from Filtering, it may be good
to have different Sampling rates). If Filtering follows a Sampling
process, the same Selection Fraction and type are applied to the
whole stream, independently of the relative size of the streams
resulting from the Filtering function. Moreover, also packets not
destined to be selected in the Filtering operation will "load" the
Sampling function. So, in principle, Filtering before Sampling
allows a more accurate tuning of the Sampling procedure, but if
filters are too complex to work at full line rate (e.g., because they
have to access router state information), Sampling before Filtering
may be a need.
8.2. Stratified Sampling
Stratified Sampling is one example for using a composite technique.
The basic idea behind stratified Sampling is to increase the
estimation accuracy by using a priori information about correlations
of the investigated characteristic with some other characteristic
that is easier to obtain. The a priori information is used to
perform an intelligent grouping of the elements of the parent
Population. In this manner, a higher estimation accuracy can be
achieved with the same sample size or the sample size can be reduced
without reducing the estimation accuracy.
Stratified Sampling divides the Sampling process into multiple steps.
First, the elements of the parent Population are grouped into subsets
in accordance to a given characteristic. This grouping can be done
in multiple steps. Then samples are taken from each subset.
The stronger the correlation between the characteristic used to
divide the parent Population (stratification variable) and the
characteristic of interest (for which an estimate is sought after),
the easier is the consecutive Sampling process and the higher is the
stratification gain. For instance, if the dividing characteristic
were equal to the investigated characteristic, each element of the
subgroup would be a perfect representative of that characteristic.
In this case, it would be sufficient to take one arbitrary element
out of each subgroup to get the actual distribution of the
characteristic in the parent Population. Therefore, stratified
Sampling can reduce the costs for the Sampling process (i.e., the
number of samples needed to achieve a given level of confidence).
For stratified Sampling, one has to specify classification rules for
grouping the elements into subgroups and the Sampling scheme that is
used within the subgroups. The classification rules can be expressed
by multiple filters. For the Sampling scheme within the subgroups,
the parameters have to be specified as described above. The use of
stratified Sampling methods for measurement purposes is described for
instance in [ClPB93] and [Zseb03].
9. Security Considerations
Security considerations concerning the choice of a Hash Function for
Hash-based Selection have been discussed in Section 6.2.3. That
section discussed a number of potential attacks to craft Packet
Streams that are disproportionately detected and/or discover the Hash
Function parameters, the vulnerabilities of different Hash Functions
to these attacks, and practices to minimize these vulnerabilities.
In addition to this, a user can gain knowledge about the start and
stop triggers in time-based systematic Sampling, e.g., by sending
test packets. This knowledge might allow users to modify their send
schedule in a way that their packets are disproportionately selected
or not selected [GoRe07].
For random Sampling, a cryptographically strong random number
generator should be used in order to prevent that an advisory can
predict the selection decision [GoRe07].
Further security threats can occur when Sampling parameters are
configured or communicated to other entities. The configuration and
reporting of Sampling parameters are out of scope of this document.
Therefore, the security threats that originate from this kind of
communication cannot be assessed with the information given in this
document.
Some of these threats can probably be addressed by keeping
configuration information confidential and by authenticating entities
that configure Sampling. Nevertheless, a full analysis and
assessment of threats for configuration and reporting has to be done
if configuration or reporting methods are proposed.
10. Contributors
Sharon Goldberg contributed to the security considerations for Hash-
based Selection.
Sharon Goldberg
Department of Electrical Engineering
Princeton University
F210-K EQuad
Princeton, NJ 08544,
USA
EMail: goldbe@princeton.edu
11. Acknowledgments
We would like to thank the PSAMP group, especially Benoit Claise and
Stewart Bryant, for fruitful discussions and for proofreading the
document. We thank Sharon Goldberg for her input on security issues
concerning Hash-based Selection.
12. References
12.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
12.2. Informative References
[AmCa89] Paul D. Amer, Lillian N. Cassel, "Management of Sampled
Real-Time Network Measurements", 14th Conference on Local
Computer Networks, October 1989, Minneapolis, pages 62-68,
IEEE, 1989.
[BeCK96] M. Bellare, R. Canetti and H. Krawczyk, "Pseudorandom
Functions Revisited: The Cascade Construction and its
Concrete Security", Symposium on Foundations of Computer
Science, 1996.
[ClPB93] K.C. Claffy, George C. Polyzos, Hans-Werner Braun,
"Application of Sampling Methodologies to Network Traffic
Characterization", Proceedings of ACM SIGCOMM'93, San
Francisco, CA, USA, September 13 - 17, 1993.
[DuGG02] N.G. Duffield, A. Gerber, M. Grossglauser, "Trajectory
Engine: A Backend for Trajectory Sampling", IEEE Network
Operations and Management Symposium 2002, Florence, Italy,
April 15-19, 2002.
[DuGr00] N.G. Duffield, M. Grossglauser, "Trajectory Sampling for
Direct Traffic Observation", Proceedings of ACM SIGCOMM
2000, Stockholm, Sweden, August 28 - September 1, 2000.
[DuGr04] N.G. Duffield and M. Grossglauser "Trajectory Sampling
with Unreliable Reporting", Proc IEEE Infocom 2004, Hong
Kong, March 2004.
[DuLT01] N.G. Duffield, C. Lund, and M. Thorup, "Charging from
Sampled Network Usage", ACM Internet Measurement Workshop
IMW 2001, San Francisco, USA, November 1-2, 2001.
[EsVa01] C. Estan and G. Varghese, "New Directions in Traffic
Measurement and Accounting", ACM SIGCOMM Internet
Measurement Workshop 2001, San Francisco (CA) Nov. 2001.
[GoRe07] S. Goldberg, J. Rexford, "Security Vulnerabilities and
Solutions for Packet Sampling", IEEE Sarnoff Symposium,
Princeton, NJ, May 2007.
[HT52] D.G. Horvitz and D.J. Thompson, "A Generalization of
Sampling without replacement from a Finite Universe" J.
Amer. Statist. Assoc. Vol. 47, pp. 663-685, 1952.
[Henk08] Christian Henke, Evaluation of Hash Functions for
Multipoint Sampling in IP Networks, Diploma Thesis, TU
Berlin, April 2008.
[HeSZ08] Christian Henke, Carsten Schmoll, Tanja Zseby, Evaluation
of Header Field Entropy for Hash-Based Packet Selection,
Proceedings of Passive and Active Measurement Conference
PAM 2008, Cleveland, Ohio, USA, April 2008.
[Jenk97] B. Jenkins, "Algorithm Alley", Dr. Dobb's Journal,
September 1997.
http://burtleburtle.net/bob/hash/doobs.html.
[JePP92] Jonathan Jedwab, Peter Phaal, Bob Pinna, "Traffic
Estimation for the Largest Sources on a Network, Using
Packet Sampling with Limited Storage", HP technical
report, Managemenr, Mathematics and Security Department,
HP Laboratories, Bristol, March 1992,
http://www.hpl.hp.com/techreports/92/HPL-92-35.html.
[Moli03] M. Molina, "A scalable and efficient methodology for flow
monitoring in the Internet", International Teletraffic
Congress (ITC-18), Berlin, Sep. 2003.
[MoND05] M. Molina, S. Niccolini, N.G. Duffield, "A Comparative
Experimental Study of Hash Functions Applied to Packet
Sampling", International Teletraffic Congress (ITC-19),
Beijing, August 2005.
[RFC1141] Mallory, T. and A. Kullberg, "Incremental updating of the
Internet checksum", RFC 1141, January 1990.
[RFC1624] Rijsinghani, A., Ed., "Computation of the Internet
Checksum via Incremental Update", RFC 1624, May 1994.
[RFC2205] Braden, R., Ed., Zhang, L., Berson, S., Herzog, S., and S.
Jamin, "Resource ReSerVation Protocol (RSVP) -- Version 1
Functional Specification", RFC 2205, September 1997.
[RFC3704] Baker, F. and P. Savola, "Ingress Filtering for Multihomed
Networks", BCP 84, RFC 3704, March 2004.
[RFC3917] Quittek, J., Zseby, T., Claise, B., and S. Zander,
"Requirements for IP Flow Information Export (IPFIX)", RFC
3917, October 2004.
[RFC4271] Rekhter, Y., Ed., Li, T., Ed., and S. Hares, Ed., "A
Border Gateway Protocol 4 (BGP-4)", RFC 4271, January
2006.
[RFC5101] Claise, B., Ed., "Specification of the IP Flow Information
Export (IPFIX) Protocol for the Exchange of IP Traffic
Flow Information", RFC 5101, January 2008.
[RFC5102] Quittek, J., Bryant, S., Claise, B., Aitken, P., and J.
Meyer, "Information Model for IP Flow Information Export",
RFC 5102, January 2008.
[RFC5474] Duffield, N., Ed., "A Framework for Packet Selection and
Reporting", RFC 5474, March 2009.
[RFC5476] Claise, B., Ed., "Packet Sampling (PSAMP) Protocol
Specifications", RFC 5476, March 2009.
[RFC5477] Dietz, T., Claise, B., Aitken, P., Dressler, F., and G.
Carle, "Information Model for Packet Sampling Exports",
RFC 5477, March 2009.
[Zseb03] T. Zseby, "Stratification Strategies for Sampling-based
Non-intrusive Measurement of One-way Delay", Proceedings
of Passive and Active Measurement Workshop (PAM 2003), La
Jolla, CA, USA, pp. 171-179, April 2003.
[ZsZC01] Tanja Zseby, Sebastian Zander, Georg Carle. Evaluation of
Building Blocks for Passive One-way-delay Measurements.
Proceedings of Passive and Active Measurement Workshop
(PAM 2001), Amsterdam, The Netherlands, April 23-24, 2001.
Appendix A. Hash Functions
A.1. IP Shift-XOR (IPSX) Hash Function
The IPSX Hash Function is tailored for acting on IP version 4
packets. It exploits the structure of IP packets and in particular
the variability expected to be exhibited within different fields of
the IP packet in order to furnish a hash value with little apparent
correlation with individual packet fields. Fields from the IPv4 and
TCP/UDP headers are used as input. The IPSX Hash Function uses a
small number of simple instructions.
Input parameters: None
Built-in parameters: None
Output: The output of the IPSX is a 16-bit number
Functioning:
The functioning can be divided into two parts: input selection, whose
forms are composite input from various portions of the IP packet,
followed by computation of the hash on the composite.
Input Selection:
The raw input is drawn from the first 20 bytes of the IP packet
header and the first 8 bytes of the IP payload. If IP options are
not used, the IP header has 20 bytes, and hence the two portions
adjoin and comprise the first 28 bytes of the IP packet. We now use
the raw input as four 32-bit subportions of these 28 bytes. We
specify the input by bit offsets from the start of IP header or
payload.
f1 = bits 32 to 63 of the IP header, comprising the IP identification
field, flags, and fragment offset.
f2 = bits 96 to 127 of the IP header, the source IP address.
f3 = bits 128 to 159 of the IP header, the destination IP address.
f4 = bits 32 to 63 of the IP payload. For a TCP packet, f4 comprises
the TCP sequence number followed by the message length. For a
UDP packet, f4 comprises the UDP checksum.
Hash Computation:
The hash is computed from f1, f2, f3, and f4 by a combination of XOR
(^), right shift (>>), and left shift (<<) operations. The
intermediate quantities h1, v1, and v2 are 32-bit numbers.
1. v1 = f1 ^ f2;
2. v2 = f3 ^ f4;
3. h1 = v1 << 8;
4. h1 ^= v1 >> 4;
5. h1 ^= v1 >> 12;
6. h1 ^= v1 >> 16;
7. h1 ^= v2 << 6;
8. h1 ^= v2 << 10;
9. h1 ^= v2 << 14;
10. h1 ^= v2 >> 7;
The output of the hash is the least significant 16 bits of h1.
A.2. BOB Hash Function
The BOB Hash Function is a Hash Function designed for having each bit
of the input affecting every bit of the return value and using both
1-bit and 2-bit deltas to achieve the so-called avalanche effect
[Jenk97]. This function was originally built for hash table lookup
with fast software implementation.
Input parameters:
The input parameters of such a function are:
- the length of the input string (key) to be hashed, in bytes.
The elementary input blocks of BOB hash are the single bytes;
therefore, no padding is needed.
- an init value (an arbitrary 32-bit number).
Built-in parameters:
The BOB hash uses the following built-in parameter:
- the golden ratio (an arbitrary 32-bit number used in the Hash
Function computation: its purpose is to avoid mapping all zeros
to all zeros).
Note: The mix sub-function (see mix (a,b,c) macro in the reference
code below) has a number of parameters governing the shifts in the
registers. The one presented is not the only possible choice.
It is an open point whether these may be considered additional
built-in parameters to specify at function configuration.
Output:
The output of the BOB function is a 32-bit number. It should be
specified:
- A 32-bit mask to apply to the output
- The Selection Range as a list of non-overlapping intervals
[start value, end value] where value is in [0,2^32]
Functioning:
The hash value is obtained computing first an initialization of an
internal state (composed of three 32-bit numbers, called a, b, c in
the reference code below), then, for each input byte of the key the
internal state is combined by addition and mixed using the mix sub-
function. Finally, the internal state mixed one last time and the
third number of the state (c) is chosen as the return value.
typedef unsigned long int ub4; /* unsigned 4-byte quantities
*/
typedef unsigned char ub1; /* unsigned 1-byte quantities
*/
#define hashsize(n) ((ub4)1<<(n))
#define hashmask(n) (hashsize(n)-1)
/* ------------------------------------------------------
mix -- mix three 32-bit values reversibly.
For every delta with one or two bits set, and the deltas of
all three high bits or all three low bits, whether the original
value of a,b,c is almost all zero or is uniformly distributed,
* If mix() is run forward or backward, at least 32 bits in
a,b,c have at least 1/4 probability of changing.
* If mix() is run forward, every bit of c will change between
1/3 and 2/3 of the time (well, 22/100 and 78/100 for some 2-
bit deltas) mix() was built out of 36 single-cycle latency
instructions in a structure that could support 2x parallelism,
like so:
a -= b;
a -= c; x = (c>>13);
b -= c; a ^= x;
b -= a; x = (a<<8);
c -= a; b ^= x;
c -= b; x = (b>>13);
...
Unfortunately, superscalar Pentiums and Sparcs can't take
advantage of that parallelism. They've also turned some of
those single-cycle latency instructions into multi-cycle latency
instructions
------------------------------------------------------------*/
#define mix(a,b,c) \
{ \
a -= b; a -= c; a ^= (c>>13); \
b -= c; b -= a; b ^= (a<<8); \
c -= a; c -= b; c ^= (b>>13); \
a -= b; a -= c; a ^= (c>>12); \
b -= c; b -= a; b ^= (a<<16); \
c -= a; c -= b; c ^= (b>>5); \
a -= b; a -= c; a ^= (c>>3); \
b -= c; b -= a; b ^= (a<<10); \
c -= a; c -= b; c ^= (b>>15); \
}
/* -----------------------------------------------------------
hash() -- hash a variable-length key into a 32-bit value
k : the key (the unaligned variable-length array of bytes)
len : the length of the key, counting by bytes
initval : can be any 4-byte value
Returns a 32-bit value. Every bit of the key affects every bit
of the return value. Every 1-bit and 2-bit delta achieves
avalanche. About 6*len+35 instructions.
The best hash table sizes are powers of 2. There is no need to do
mod a prime (mod is so slow!). If you need less than 32 bits, use a
bitmask. For example, if you need only 10 bits, do h = (h &
hashmask(10)), in which case, the hash table should have hashsize(10)
elements.
If you are hashing n strings (ub1 **)k, do it like this: for (i=0,
h=0; i<n; ++i) h = hash( k[i], len[i], h);
By Bob Jenkins, 1996. bob_jenkins@burtleburtle.net. You may use
this code any way you wish, private, educational, or commercial.
It's free. See http://burtleburtle.net/bob/hash/evahash.html.
Use for hash table lookup, or anything where one collision in 2^^32
is acceptable. Do NOT use for cryptographic purposes.
----------------------------------------------------------- */
ub4 bob_hash(k, length, initval)
register ub1 *k; /* the key */
register ub4 length; /* the length of the key */
register ub4 initval; /* an arbitrary value */
{
register ub4 a,b,c,len;
/* Set up the internal state */
len = length;
a = b = 0x9e3779b9; /*the golden ratio; an arbitrary value
*/
c = initval; /* another arbitrary value */
/*------------------------------------ handle most of the key */
while (len >= 12)
{
a += (k[0] +((ub4)k[1]<<8) +((ub4)k[2]<<16)
+((ub4)k[3]<<24));
b += (k[4] +((ub4)k[5]<<8) +((ub4)k[6]<<16)
+((ub4)k[7]<<24));
c += (k[8] +((ub4)k[9]<<8)
+((ub4)k[10]<<16)+((ub4)k[11]<<24));
mix(a,b,c);
k += 12; len -= 12;
}
/*---------------------------- handle the last 11 bytes */
c += length;
switch(len) /* all the case statements fall through*/
{
case 11: c+=((ub4)k[10]<<24);
case 10: c+=((ub4)k[9]<<16);
case 9 : c+=((ub4)k[8]<<8);
/* the first byte of c is reserved for the length */
case 8 : b+=((ub4)k[7]<<24);
case 7 : b+=((ub4)k[6]<<16);
case 6 : b+=((ub4)k[5]<<8);
case 5 : b+=k[4];
case 4 : a+=((ub4)k[3]<<24);
case 3 : a+=((ub4)k[2]<<16);
case 2 : a+=((ub4)k[1]<<8);
case 1 : a+=k[0];
/* case 0: nothing left to add */
}
mix(a,b,c);
/*-------------------------------- report the result */
return c;
}
Authors' Addresses
Tanja Zseby
Fraunhofer Institute for Open Communication Systems
Kaiserin-Augusta-Allee 31
10589 Berlin
Germany
Phone: +49-30-34 63 7153
EMail: tanja.zseby@fokus.fraunhofer.de
Maurizio Molina
DANTE
City House
126-130 Hills Road
Cambridge CB21PQ
United Kingdom
Phone: +44 1223 371 300
EMail: maurizio.molina@dante.org.uk
Nick Duffield
AT&T Labs - Research
Room B-139
180 Park Ave
Florham Park, NJ 07932
USA
Phone: +1 973-360-8726
EMail: duffield@research.att.com
Saverio Niccolini
Network Laboratories, NEC Europe Ltd.
Kurfuerstenanlage 36
69115 Heidelberg
Germany
Phone: +49-6221-9051118
EMail: saverio.niccolini@netlab.nec.de
Frederic Raspall
EPSC-UPC
Dept. of Telematics
Av. del Canal Olimpic, s/n
Edifici C4
E-08860 Castelldefels, Barcelona
Spain
EMail: fredi@entel.upc.es