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Network Working Group                                           T. Zseby
Request for Comments: 5475                              Fraunhofer FOKUS
Category: Standards Track                                      M. Molina
                                                             N. Duffield
                                                    AT&T Labs - Research
                                                            S. Niccolini
                                                         NEC Europe Ltd.
                                                              F. Raspall
                                                              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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   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

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   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

   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.

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1.1.  Conventions Used in This Document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   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

   [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;

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        (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.

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   * 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

       (iii) iterators for pseudorandom number generators;

        (iv) hash values calculated during selection;

         (v) indicators of whether the packet was selected by a given

      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

   * 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.

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   * 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

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].

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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

   * 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.

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   * 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.

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   * 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

   * 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

   * 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.

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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.

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        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

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   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

   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.

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   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

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

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   non-uniform probabilistic Sampling (where the selection probability
   can vary for different packets).  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.  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].  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.

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   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).  Configuration of Non-Uniform Probabilistic and Flow State

   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

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   attributes specified in [RFC5102].  Further fields can be defined by
   proposing new information elements or defining vendor-specific

   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

        (ii) transport protocol header (e.g., TCP, UDP)

       (iii) encapsulation headers (e.g., the MPLS label stack, if

   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.

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   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

      (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

   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.

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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  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

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   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.  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.  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

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   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 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.  Requirements for Packet Selection

   In accordance to considerations in [MoND05] and [Henk08], we define
   the following desired properties of Hash Functions used for packet

         (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.

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        (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.  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.

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   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

   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].  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

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   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.  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.

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        (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

   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.  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.

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   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].

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RFC 5475           Techniques for IP Packet Selection         March 2009  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

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   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

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   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.  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.

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     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:


   Unique ID for the packet sampler.

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   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

   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

   Case flow state:
      - Information reported for flow state Sampling is not defined in
        this document (see also Section

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:

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   Unique ID for the packet filter.  The ID can be calculated under
   consideration of the SELECTION SEQUENCE and a local ID.

   For Filtering processes, the SELECTOR TYPE defines what Filtering
   type is used.
   Values: Matching | Hashing | Router_state

   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


   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
           - 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.

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   - 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

   - Egress interface to which the packet is routed equals a specified

   - 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

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   - Destination AS equals a specified value or lies within a given

   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.

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   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

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   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,
   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.

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   [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.

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   [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,

   [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

   [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.

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RFC 5475           Techniques for IP Packet Selection         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.

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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


   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

   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.

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   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).

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   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.


   The output of the BOB function is a 32-bit number.  It should be

      - 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]


   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:

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           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


     #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)

   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);

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   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)
           b += (k[4] +((ub4)k[5]<<8) +((ub4)k[6]<<16)
           c += (k[8] +((ub4)k[9]<<8)
           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);

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        case 2 : a+=((ub4)k[1]<<8);
        case 1 : a+=k[0];
          /* case 0: nothing left to add */
        /*-------------------------------- report the result */
        return c;

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Authors' Addresses

   Tanja Zseby
   Fraunhofer Institute for Open Communication Systems
   Kaiserin-Augusta-Allee 31
   10589 Berlin
   Phone: +49-30-34 63 7153
   EMail: tanja.zseby@fokus.fraunhofer.de

   Maurizio Molina
   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
   Phone: +1 973-360-8726
   EMail: duffield@research.att.com

   Saverio Niccolini
   Network Laboratories, NEC Europe Ltd.
   Kurfuerstenanlage 36
   69115 Heidelberg
   Phone: +49-6221-9051118
   EMail:  saverio.niccolini@netlab.nec.de

   Frederic Raspall
   Dept. of Telematics
   Av. del Canal Olimpic, s/n
   Edifici C4
   E-08860 Castelldefels, Barcelona
   EMail: fredi@entel.upc.es

Zseby, et al.               Standards Track                    [Page 46]