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 Internet Draft
 Document: <draft-ietf-psamp-sample-tech-07.txt>                T. Zseby
 Expires: January 2006                                  Fraunhofer FOKUS
                                                               M. Molina
                                                                   DANTE
                                                             N. Duffield
                                                     AT&T Labs-Research
                                                            S. Niccolini
                                                         NEC Europe Ltd.
                                                              F. Raspall
                                                                EPSC-UPC
 
                                                               July 2005
 
 
    Sampling and Filtering Techniques for IP Packet Selection
 
 
 Status of this Memo
 
    By submitting this Internet-Draft, each author represents that
    any applicable patent or other IPR claims of which he or she is
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 Copyright Notice
 
    Copyright (C) The Internet Society (2005).
 
 
 Abstract
 
 
 
 
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    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 Measurement Processes and
    for reporting the technique in use to a Collector.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 Table of Contents
 
    1.   Introduction.................................................4
    2.   PSAMP Documents Overview.....................................4
    3.   Terminology..................................................5
    3.1     Observation Points, Packet Streams and Packet Content.....5
    3.2     Selection Process.........................................6
    3.3     Reporting Process.........................................7
    3.4     Measurement Process.......................................8
    3.5     Exporting Process.........................................8
    3.6     PSAMP Device..............................................9
    3.7     Collector.................................................9
    3.8     Selection Methods.........................................9
    4.   Categorization of Packet Selection Techniques...............11
    5.   Sampling....................................................13
    5.1     Systematic Sampling......................................14
    5.2     Random Sampling..........................................15
    5.2.1   n-out-of-N Sampling......................................15
    5.2.2   Probabilistic Sampling...................................15
    5.2.2.1 Uniform Probabilistic Sampling...........................15
    5.2.2.2 Non-Uniform Probabilistic Sampling.......................16
    5.2.2.3 Non-Uniform Flow State Dependent Sampling................16
    5.2.2.4 Configuration of non-uniform probabilistic and flow-
             state Sampling..........................................17
    6.   Filtering...................................................17
    6.1     Field Match Filtering....................................17
    6.2     Hash-based Filtering.....................................18
    6.2.1   Application Examples for Hash-based Selection............18
    6.2.1.1 Approximation of Random Sampling.........................18
    6.2.1.2 Trajectory Sampling and Consistent packet selection......19
    6.2.2   Guarding Against Pitfalls and Vulnerabilities............20
    6.2.3   Recommended Hash Functions...............................20
    6.2.3.1 Hash Functions Suitable for Packet Selection.............21
    6.2.3.2 Input Bytes for the BOB Hash Function for IPv4...........21
    6.2.3.3 Input Bytes for the BOB Hash Function for IPv6...........22
    6.2.3.4 Hash Functions Suitable for Packet Digesting.............22
    6.3     Router State Filtering...................................23
    7.   Parameters for the Description of Selection Techniques......23
    7.1     Description of Sampling Techniques.......................24
    7.2     Description of Filtering Techniques......................26
    8.   Composite Techniques........................................28
    8.1     Cascaded Filtering->Sampling or Sampling->Filtering......28
    8.2     Stratified Sampling......................................29
    9.   Security Considerations.....................................29
    10.  Acknowledgements............................................30
    11.  Normative References........................................30
    12.  Informative References......................................31
    13.  Authors' Addresses..........................................33
 
 
 
 
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    14.  Intellectual Property Statement.............................33
    15.  Copyright Statement.........................................34
    16.  Disclaimer..................................................34
    17.  Appendix: Hash Functions....................................34
    17.1    IP Shift-XOR (IPSX) Hash Function........................35
    17.2    BOB Hash Function........................................36
 
 1. Introduction
 
    There are two main drivers for the growth in measurement
    infrastructures and their underlying technology. First, network
    data rates are increasing, with a concomitant growth in
    measurement data. Secondly, the growth is compounded by the
    demand by measurement-based applications for increasingly fine
    grained traffic measurements. Devices such as routers, which
    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, transport 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, Filtering, or aggregation.
    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 [PSAMP-FW]. Aggregation techniques are
    out of scope of this document.
 
 2. PSAMP Documents Overview
 
    [PSAMP-FW]:   "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.
 
 
 
 
 
 
 
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    [PSAMP-TECH]: "Sampling and Filtering Techniques for IP Packet
                   Selection" (this document) describes the set of
                   packet selection techniques supported by PSAMP.
 
    [PSAMP-MIB]:  "Definitions of Managed Objects for Packet
                   Sampling" describes the PSAMP Management
                   Information Base.
 
    [PSAMP-PROTO]: "Packet Sampling (PSAMP) Protocol Specifications"
                   specifies the export of packet information from a
                   PSAMP Exporting Process to a PSAMP Colleting
                   Process.
 
    [PSAMP-INFO]: "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 [PSAMP-FW] 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 [PSAMP-FW]. PSAMP terminology also aims
    to be consistent with terms used in [IPFIX-REQ]. The
    relationship between some PSAMP and IPFIX terms is described in
    [PSAMP-FW].
 
 3.1 Observation Points, Packet Streams and Packet Content
 
    * Observation Point
 
       An Observation Point 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
 
 
 
 
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       The Observed Packet Stream is the set of all packets observed
       at the Observation Point.
 
    * Packet Stream
 
       A packet stream denotes a subset of the Observed Packet Stream
       that flows past some specified point within the measurement
       process. An example of a Packet Stream is the output of the
       selection process.
 
    * Packet Stream
 
       A packet stream denotes a set of packets that flows past some
       specified point within the measurement 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 set of packets with common properties [IPFIX-REQ].
 
    * 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.
 
 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 and/or the reporting 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;
 
 
 
 
 
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         (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 the action of a Selection Process 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's 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.
 
 3.3 Reporting Process
 
    * Reporting Process:
 
       A Reporting Process creates a Report Stream on packets
       selected by a Selection Process, in preparation for export.
       The input to the Reporting Process comprises that information
       available to the Selection Process per selected packet,
       specifically:
 
         (i)  the selected packet's content;
 
 
 
 
 
 
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         (ii) information derived from the selected packet's
               treatment at the Observation Point;
 
         (iii) any Selection State maintained by the inputting
               Selection Process, reflecting any modifications to the
               Selection State made during selection of the packet.
 
    * Packet Reports
 
       Packet Reports comprise a configurable subset of a packet's
       input to the reporting 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 and of the
       Reporting Process.
 
    * Report Stream:
 
       The Report Stream is the output of a Reporting Process,
       comprising two distinguished types of information: Packet
       Reports, and Report Interpretation.
 
 3.4 Measurement Process
 
    * Measurement Process
 
       A Measurement Process is the composition of a Selection
       Process that takes the Observed Packet Stream as its input,
       followed by a Reporting Process.
 
 3.5 Exporting Process
 
    * Exporting Process:
 
       An Exporting Process sends, in the form of Export Packet, the
       output of one or more Measurement Processes to one or more
       Collectors.
 
    * Export Packet:
 
 
 
 
 
 
 
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       An Export Packet is a combination of Report Interpretation
       and/or one or more Packet Reports are bundled by the Exporting
       Process into a 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 Measurement Process and an Exporting Process.
       Typically, corresponding Observation Point(s), Measurement
       Process(es) and Exporting Process(es) are co-located 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
       Measurement 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. Examples include field
       match Filtering, and Hash-based Selection.
 
    * 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.
 
    * Content-independent Sampling
 
       A Sampling operation that does not use Packet Content (or
       quantities derived from it) as the basis for selection is
       called a Content-independent Sampling operation. 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.
 
 
 
 
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    * Content-dependent Sampling
 
       A Sampling operation where selection is dependent on Packet
       Content is called a Content-dependent Sampling operation.
       Examples include pseudorandom selection according to a
       probability that depends on the contents of a packet field.
       Note that this is not a filter, because the selection is not
       deterministic.
 
    * Hash Domain
 
       A subset of the Packet Content and the packet treatment,
       viewed as an N-bit string for some positive integer N.
 
    * Hash Range
 
       A set of M-bit strings for some positive integer M that define
       the range of values the result of the hash operation can take.
 
    * Hash Function
 
       A deterministic map from the Hash Domain into the Hash Range.
 
    * Hash Selection Range
 
       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
 
       Filtering specified by a Hash Domain, a Hash Function, and
       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
 
 
 
 
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       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 number of packets selected from the Population by a
       Selector.
 
    * Configured Selection Fraction
 
       The Configured Selection Fraction is the ratio of the number
       of packets selected by a Selector from an input Population, to
       the Population Size, as based on the configured selection
       parameters.
 
    * Attained Selection Fraction
 
       The Attained Selection Fraction is the actual ratio of the
       number of packets selected by a Selector from an input
       Population, 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
 
 
 
 
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    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. An X in brackets (X)
    denotes schemes for which also content-independent variants
    exist. 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 |               |          |
 
 
 
 
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      ------------------------+---------------+----------+----------
       Field match filtering  |       X       |     X    | Filtering
      ------------------------+---------------+----------+----------
       Hash Function          |       X       |     X    | Filtering
      ------------------------+---------------+----------+----------
       Router state filtering |       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].
 
    Secondly, 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).
 
 
 
 
 
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    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 pre-defined 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).
 
 
 
 
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    Both schemes are content-independent selection schemes. Content
    dependent deterministic Selectors are categorized as filter.
 
 5.2 Random Sampling
 
    Random Sampling selects the starting points of the Sampling
    intervals in accordance to a random process. The selection of
    elements are independent experiments. 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:
 
 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 which 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 an element is
    selected or not is made in accordance to a pre-defined 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 has 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 are independent random variables 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
 
 
 
 
 
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    where the intervals or counts between successive samples are
    independent identically distributed random variable.
 
 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 renormalization 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 [IPFIX-REQ], 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 to 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 [PSAMP-FW]
    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). See [MolFl03] for a more
    detailed description of the motivations for this type of
    Sampling and the impact on the IPFIX metering.
 
 
 
 
 
 
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 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 is required to configure such
    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 amongst 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 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 the space, or on a random process.
    We identify and describe in the following three Filtering
    techniques. The first two (Match Filtering and Hashing
    Filtering) are stateless, and therefore can make their decision
    based on the analysis of portion of the packet only. The other
    (router state Filtering) requires to access state information
    after the analysis of part of the packet and is therefore more
    complex: its usage makes sense only in particular circumstances,
    as described below.
 
 6.1 Field Match Filtering
 
    We here define a basic Filtering schemes based on the IPIFIX
    flow definition. With this method a packet is selected if a
    specific field in the packet equals a predefined value. Possible
    filter fields are all IPFIX flow attributes specified in [IPFIX-
    INFO]. Further fields can be defined by vendor specific
    extensions.
    A packet is selected if Field=Value. Masks and ranges are only
    supported to the extend to which [IPFIX-INFO] allows them e.g.
 
 
 
 
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    by providing explicit fields like the netmasks for source and
    destination addresses. AND operations are possible by
    concatenating filters. OR operations are not supported with this
    basic model. More sophisticated filters (e.g. supporting
    bitmasks, ranges or OR operations etc.) can be realized as
    vendor specific schemes.
 
 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 indeed 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 field match filter or a simple combination of these.
    Hash-based selection has mainly two types of usage: it offers a
    way to approximate random Sampling by using packet content to
    generate pseudorandom variates, and a way to consistently select
    subsets of packets that share a common property (e.g. at
    different Observation Points).
 
    In the following subsections we give more details about them.
    However, both usages require that the Hash Functions has two
    statistical properties.
 
    First, the Hash Function h must 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 Sampling Fraction
    is #S/#R, which can be tuned by choice of S.
 
    The second desirable property depends more closely on the
    statistics of the content c. In applications, the content c
    comprises a number of distinct fields, c1 ... cm, e.g. source
    and destination IP Address, IP identification, and TCP/UDP port
    numbers (if present) for a packet. With a Hash Function
    satisfying the first properties above, selection decisions will
    appear uncorrelated with the contents of any individual field,
    if the complementary fields are (i) sufficiently variable
    themselves, and (ii) sufficiently uncorrelated with cj.
 
 6.2.1   Application Examples for Hash-based Selection
 
 6.2.1.1 Approximation of Random Sampling
 
 
 
 
 
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    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
    renormalized) 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 3.1.2.2.2
    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.
 
 6.2.1.2 Trajectory Sampling and Consistent packet selection.
 
    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, 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 quasirandomly
    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 observations points are
    associated by matching a label from the reports. The label may
    comprise that portion 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
    (distinct from that used for selection) 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
 
 
 
 
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    loss of reports in transmission, are dealt with in [DuGr01],
    [DuGeGr02] 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; (iv) network attack
    tracing, of the actual paths taken by attack packets with
    spoofed source addresses.
 
 6.2.2   Guarding Against Pitfalls and Vulnerabilities
 
    A concern for Hash-based Selection is whether some large set of
    related packets could have an Attained Sampling Fraction
    significantly different from the Configured Sampling Fraction,
    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. Examples of such are CRC32
    and Hash Functions based on modular arithmetic, see 6.4 in
    [Knuth98]. The statistical properties of candidate Hash
    Functions need to be evaluated, preferably on packet traces
    before adoption for hash-based Sampling
 
    Hash-based selection could be overloaded or evaded by an
    attacker if the Hash Function and the selection range are both
    known. A service provider could build a first defense keeping
    the Hash Selection Range S private. Then, an attacker could not
    determine whether a crafted packet is selected, but would still
    know that a crafted set of packets all with the same hash is
    either all selected or all not selected. A stronger defense is
    to employ a parametrizable Hash Function and keep the parameter
    private. Without knowledge of the parameter, a set of packets
    with common hash value cannot be constructed. Examples of
    parameters are the initial vector in CRC32, and moduli in hashes
    based on modular arithmetic.
 
 6.2.3   Recommended Hash Functions
 
    We here indicate some Hash Functions that can be used for packet
    selection. The description of the IPSX and BOB Hash Functions
    can be found in the appendix. The CRC-32 function is described
    in [crc32]. The comparison of hash-functions with regard to
 
 
 
 
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    collision probability, the randomness of the packet selection
    (i.e. the uniformity of the distribution of values in the Hash
    Range) and the speed of the functions is described in [MoND05].
    Although the uniformity has been checked for different traffic
    traces, results cannot be generalized to arbitrary traffic.
    Since the hash-based selection is a deterministic function on
    the packet content, it can always be biased towards packets with
    specific attributes. Furthermore, please note that all Hash
    Functions were evaluated only for IPv4.
 
 6.2.3.1 Hash Functions Suitable for Packet Selection
 
    For hash-based packet selection, the most important requirements
    for the Hash Function are high execution speed, because the
    selection must operate at line rate, and the uniformity of the
    distribution of values in the Hash Range in order to provide a
    good emulation of a random packet selection.
 
    The IPSX function is simple and easy to implement. The
    evaluation results showed that the IPSX function is faster than
    the BOB function. Nevertheless, IPSX is limited to 16 bytes
    input. All investigated Hash Functions showed a poor uniformity
    if only 16 bytes are used as input. Therefore, if a hash-based
    packet selection is implemented, the BOB function SHOULD be used
    for packet selection operations. Other functions, like IPSX, MAY
    be used.
 
    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.
 
 6.2.3.2 Input Bytes for the BOB Hash Function for IPv4
 
    All investigated Hash Functions showed a poor uniformity if only
    16 bytes are used as input. If the input consists of 20 bytes
    which include parts of the IP packet payload (i.e. everything
    following the IP header) the uniformity was significantly
    improved. 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
 
 
 
 
 
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    - A configurable number of bytes from the IP payload, starting
       at a configurable offset.
 
    Please note that the uniformity increases with the number of
    additional input bytes from the IP payload. The default setting
    is to use 4 bytes from the IP payload. Using less bytes can lead
    to a significant bias in the selection.
 
 6.2.3.3 Input Bytes for the BOB Hash Function for IPv6
 
    All investigated Hash Functions 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 was not evaluated in the tests mentioned above. In
    addition to this, IPv6 traffic profiles may change significantly
    in 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 it 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. It is possible that the selection is
    biased towards packets with specific attributes.
 
 6.2.3.4 Hash Functions Suitable 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.
 
 
 
 
 
 
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    For this purpose also the BOB function is recommended. 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.
 
 6.3 Router State Filtering
 
    This class of filters selects a packet on the basis of router
    state conditions. The following list gives examples for such
    conditions. Conditions can be combined with AND operators.
 
       - 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 found for the packet
       - Origin BGP AS [RFC1771] equals a specified value or lies
          within a given  range
       - Destination BGP AS equals a specified value or lies within
          a given range
 
    Router architectural considerations may preclude some
    information concerning the packet treatment, e.g. routing state,
    being available at line rate for selection of packets. However,
    if selection not based on routing state has reduced down from
    line rate, subselection based on routing state may be feasible.
 
 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 to select a packet or not is based on a
    function which 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
 
 
 
 
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        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.
       ---------------+------------------------+-------------------
        field match   | packet content(parts)  |  filter function
       ---------------+------------------------+-------------------
        hash-based    |  packet content(parts) |  Hash Function
       ---------------+------------------------+-------------------
        router state  |    router state        |   router state
                      |                        |   discovery
       ---------------+------------------------+-------------------
 
 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 pre-defined and given by the Selector ID.
 
    Sampler Description:
         SELECTOR_ID
         SELECTOR_TYPE
         SELECTOR_PARAMETERS
         ASSOCIATIONS
 
    Where:
 
    SELECTOR_ID:
    Unique ID for the packet sampler, calculated as combination of
    the ASSOCIATIONS and a local ID.
 
    SELECTOR_TYPE
    For Sampling processes the SELECTOR TYPE defines what Sampling
    algorithm is used.
 
 
 
 
 
 
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    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 can be found in
          [MolFl03](see also section 5.2.2.4)
 
    ASSOCIATIONS
    The ASSOCIATIONS field describes the Observation Point and
    optionally the IPFIX processes to which the packet Selector is
    associated. If no IPFIX process is specified, the selector is
    applied to all IPFIX processes on the observation point. The
    STREAM ID denotes the origin of the data stream that is input to
    the selection function. It can be the Observation Point directly
    or the ID of another Selector. With this it is possible to
    define combined schemes. If the STREAM ID contains IDs from
    other Selectors, one can derive the original Observation Point
    from the Selector definitions of these specified Selectors.
 
    Values: <STREAM ID, IPFIX Metering process ID, IPFIX Exporting
    process ID, IDs of other associated processes>
    With STREAM ID: Observation point ID AND List of SELECTOR_IDs
 
 
 
 
 
 
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 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
         ASSOCIATIONS
 
    Where:
 
    SELECTOR_ID:
    Unique ID for the packet filter. The ID can be calculated under
    consideration of the ASSOCIATIONS 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 [IPFIX-INFO])
       - Value (type in accordance to [IPFIX-INFO])
 
    In case of multiple match criteria, multiple "case matching"
    have 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)
 
 
 
 
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            - 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 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 bytes bitmask
          to be applied to the header.
       - In case of ipv6, it is a sequence of 40 Hexadecimal numbers
          [00,FF] specifying a 40 bytes 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 then 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. E.g. 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 found for the packet
 
 
 
 
 
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       - 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
 
    ASSOCIATIONS
    The ASSOCIATIONS field describes the Observation Point and
    optionally the IPFIX processes to which the packet Selector is
    associated. If no IPFIX process is specified, the Selector is
    applied to all IPFIX processes on the observation point. The
    STREAM ID denotes the origin of the data stream that is input to
    the selection function. It can be the Observation Point directly
    or the ID of another Selector. With this it is possible to
    define combined schemes. If the STREAM ID contains IDs from
    other Selectors, one can derive the original Observation Point
    from the Selector definitions of these specified Selectors.
 
    Values: <STREAM ID, IPFIX Metering process ID, IPFIX Exporting
    process ID, IDs of other associated processes>
    With STREAM ID: Observation point ID AND List of SELECTOR_IDs
 
 8. Composite Techniques
 
    Composite schemes are realized by using the STREAM ID in the
    information models. The STREAM ID denotes from which Selectors
    the input stream originates. If multiple stream IDs are given,
    this means that the Selector operates on the packet stream
    simply resulting from the time superposition of the output of
    all the listed filters and samplers. 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 Sampling Fraction
    and type is 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
 
 
 
 
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    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. With this 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 sub-group 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
 
    Malicious users or attackers may wish to hide packets from
    service providers or network operators. For instance if packet
    Selectors are used for accounting or intrusion detection
 
 
 
 
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    applications, users may want to prevent certain packets from
    being selected. If a deterministic Sampling scheme is used or a
    selection scheme that takes packet content into account, the
    user can shape or send packets in a way that they are less
    likely to be selected (see also section 6.2.2). Even if the
    selection function is unknown to the user, some insight into the
    function can be obtained by performing experiments with
    different packet sequences. This has to be taken into account
    when choosing an appropriate packet selection technique.
 
    Further security threats can occur if the configuration of
    Sampling parameters or the communication of Sampling parameters
    to the application is corrupted. This document only describes
    Sampling schemes that can be used for packet selection. It
    neither describes a mechanism how those parameters are
    configured nor how these parameters are communicated to the
    application. Therefore the security threats that originate from
    this kind of communication cannot be assessed with the
    information given in this document.
 
 10. Acknowledgements
 
    We would like to thank the PSAMP group, especially Benoit Claise
    and Stewart Bryant, for fruitful discussions and for
    proofreading the document.
 
 11. Normative References
 
    [PSAMP-FW]   Nick Duffield (Ed.): A Framework for Packet
                  Selection and Reporting, RFC XXXX [currently
                  Internet Draft draft-ietf-psamp-framework-08, work
                  in progress, October 2004]
 
    [PSAMP-MIB]  T. Dietz, B. Claise: Definitions of Managed Objects
                  for Packet Sampling, RFC XXXX. [Currently Internet
                  Draft, draft-ietf-psamp-mib-03.txt, work in
                  progress, July 2004.]
 
    [PSAMP-PROTO] B. Claise (Ed.): Packet Sampling (PSAMP) Protocol
                  Specifications, RFC XXXX. [Currently Internet Draft
                  draft-ietf-psamp-protocol-01.txt, work in progress,
                  February 2004.]
 
    [PSAMP-INFO] T. Dietz, F. Dressler, G. Carle, B. Claise:
                  Information Model for Packet Sampling Exports, RFC
                  XXXX. [Currently Internet Draft, draft-ietf-psamp-
                  info-02, July 2004]
 
 
 
 
 
 
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    [IPFIX-INFO] J. Meyer, J. Quittek, S. Bryant: Information Model
                  for IP Flow Information Export, RFC XXXX [Currently
                  Internet Draft, draft-ietf-ipfix-info-04, July
                  2004]
 
    [IPFIX-REQ]  J. Quittek, T. Zseby, B. Claise, S. Zander:
                  Requirements for IP Flow Information Export, RFC
                  3917, October 2004
 
 12. 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
 
    [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
 
    [CoGi98]    I. Cozzani, S. Giordano: Traffic Sampling Methods
                 for end-to-end QoS Evaluation in Large
                 Heterogeneous Networks. Computer Networks and ISDN
                 Systems, 30 (16-18), September 1998.
 
    [crc32]     R. Braden, D. Borman, C. Partridge: Computing the
                 Internet Checksum, RFC 1071, Sep. 1988 (updated by
                 RFCs 1141 and 1624)
 
    [DuGeGr02]  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
 
 
 
 
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    [EsVa01]    C. Estan and G. Varghese: New Directions in Traffic
                 Measurement and Accounting, ACM SIGCOMM Internet
                 Measurement Workshop 2001, San Francisco (CA) Nov.
                 2001
 
    [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.
 
    [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
 
    [Knuth98]   Donald E. Knuth: The Art of Computer Programming,
                 Volume 3: Searching and Sorting, Addison Wesley,
                 1998
 
    [MolFl03]   M.Molina: Flow selection support in IPFIX, Internet
                 Draft <draft-molina-flow-selection-00.txt>, work in
                 progress, October 2003.
 
    [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
 
 
    [RFC1771]   Rekhter, Y. and T. Li: A Border Gateway Protocol 4
                 (BGP-4), RFC 1771, March 1995.
 
    [Zseb03]    T. Zseby: Stratification Strategies for Sampling-
                 based Non-intrusive Measurement of One-way Delay.
                 Proceedings of Passive and Active Measurement
                 Workshop (PAM 20003), La Jolla, CA, USA, pp. 171-
                 179, April 2003
 
 
 
 
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 13. Authors' Addresses
 
    Tanja Zseby
    Fraunhofer Institute for Open Communication Systems
    Kaiserin-Augusta-Allee 31
    10589 Berlin
    Germany
    Phone: +49-30-34 63 7153
    Email: zseby@fokus.fhg.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
 
    Fredric Raspall
    EPSC-UPC
    Dept. of Telematics
    Av. del Canal Olimpic, s/n
    Edifici C4
    E-08860 Castelldefels, Barcelona
    Spain
    Email: fredi@entel.upc.es
 
 14. Intellectual Property Statement
 
    The IETF has been notified by AT&T Corp. of intellectual
    property rights claimed in regard to some or all of the
 
 
 
 
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    specification contained in this document. For more information,
    see http://www.ietf.org/ietf/IPR/att-ipr-draft-ietf-psamp-
    framework.txt
 
    The IETF takes no position regarding the validity or scope of
    any Intellectual Property Rights or other rights that might be
    claimed to pertain to the implementation or use of the
    technology described in this document or the extent to which any
    license under such rights might or might not be available; nor
    does it represent that it has made any independent effort to
    identify any such rights.  Information on the procedures with
    respect to rights in RFC documents can be found in BCP 78 and
    BCP 79.
 
    Copies of IPR disclosures made to the IETF Secretariat and any
    assurances of licenses to be made available, or the result of an
    attempt made to obtain a general license or permission for the
    use of such proprietary rights by implementers or users of this
    specification can be obtained from the IETF on-line IPR
    repository at http://www.ietf.org/ipr.
 
    The IETF invites any interested party to bring to its attention
    any copyrights, patents or patent applications, or other
    proprietary rights that may cover technology that may be
    required to implement this standard.  Please address the
    information to the IETF at ietf-ipr@ietf.org.
 
 15. Copyright Statement
 
    Copyright (C) The Internet Society (2005).  This document is
    subject to the rights, licenses and restrictions contained in
    BCP 78, and except as set forth therein, the authors retain all
    their rights.
 
 16. Disclaimer
 
    This document and the information contained herein are provided
    on an "AS IS" basis and THE CONTRIBUTOR, THE ORGANIZATION HE/SHE
    REPRESENTS OR IS SPONSORED BY (IF ANY), THE INTERNET SOCIETY AND
    THE INTERNET ENGINEERING TASK FORCE DISCLAIM ALL WARRANTIES,
    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY
    THAT THE USE OF THE INFORMATION HEREIN WILL NOT INFRINGE ANY
    RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS
    FOR A PARTICULAR PURPOSE.
 
 
 17. Appendix: Hash Functions
 
 
 
 
 
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 17.1    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 packet 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,
    which 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 4 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, v2 are 32-bit numbers.
 
           1.    v1 = f1 ^ f2;
 
 
 
 
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           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.
 
 
 17.2    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 in 3.2.4) 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]
 
 
 
 
 
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    Functioning:
    The hash value is obtained computing first an initialization of
    an internal state (composed of 3 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 3 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 supported 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);  \
 
 
 
 
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        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 sooo 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 */
 
 
 
 
 
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         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;
      }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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