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draft on sampling techniques
- To: psamp <psamp@ops.ietf.org>
- Subject: draft on sampling techniques
- From: Tanja Zseby <zseby@fokus.gmd.de>
- Date: Tue, 27 Aug 2002 11:37:15 +0200
- Cc: Jürgen Quittek <quittek@ccrle.nec.de>
- Organization: FhI FOKUS
- User-agent: Mozilla/5.0 (Windows; U; Windows NT 5.0; en-US; rv:0.9.4) Gecko/20011019 Netscape6/6.2
Dear psamp people,
I started a draft on sampling techniques for packet selection (document
is attached). The document tries to define some terminology and
describes various sampling methods and their parameters.
If you have any comments or if you like to contribute some text please
let me know.
I know that there were once some volunteers for writing psamp documents.
Are there people already working on other documents than the framework
draft ?
Kind regards
Tanja
--
Dipl.-Ing. Tanja Zseby
FhI FOKUS/Global Networking Email: zseby@fokus.fhg.de
Kaiserin-Augusta-Allee 31 Phone: +49-30-3463-7153
D-10589 Berlin, Germany Fax: +49-30-3463-8153
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"Living on earth is expensive but it includes a free trip around the sun." (Anonymous)
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Internet Draft
Document: <draft-zseby-packet-selection-00.txt> T. Zseby
Category: Experimental Fraunhofer FOKUS
August 2002
Sampling Techniques for Packet Selection
Status of this Memo
This document is an Internet-Draft and is in full conformance with
all provisions of Section 10 of RFC2026.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that
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The list of current Internet-Drafts can be accessed at
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The list of Internet-Draft Shadow Directories can be accessed at
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Abstract
This document describes the deployment of sampling techniques for
packet selection. It suggests some terminology and shows different
sampling methods for selecting a subset of packets in a flow.
Furthermore it describes which parameters can be varied for the
different sampling methods.
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Table of Contents
1. Introduction.................................................2
2. Terminology..................................................3
3. Deployment of Sampling Techniques for packet selection.......3
4. Sampling Methods.............................................4
4.1 Sampling Algorithm...........................................4
4.1.1Systematic Sampling..........................................4
4.1.2Random Sampling..............................................5
4.1.3Stratified Sampling..........................................5
4.2 Sampling Frequency and Interval-Length.......................6
4.2.1Count-based Trigger..........................................6
4.2.2Time-based Trigger...........................................6
4.2.3Packet-content-based Trigger.................................6
5. Sampling Parameters..........................................7
5.1 Parameters for systematic sampling...........................7
5.2 Parameters for random sampling...............................8
5.3 Parameters for stratified sampling...........................8
6. Security Considerations......................................8
7. References...................................................8
8. Author's Addresses...........................................9
9. Full Copyright Statement.....................................9
1. Introduction
Increasing data rates and growing measurement demands increase the
requirements for data collection resources. For measurement
scenarios in backbone networks it is often required to measure whole
traffic aggregates instead of single flows. Furthermore some
measurement methods require the capturing of packet headers or even
parts of the payload. All this can lead to an overwhelming amount of
measurement data, resulting in high demands regarding resources for
storage, transport and post processing.
In some cases specialized hardware helps to fulfill these demands
but on the other hand increases the costs for providing the
measurement. Since measurements are mainly a supporting
functionality for the service provisioning, measurement costs
usually should be limited to a small fraction of the costs of the
network service provisioning itself. Therefore a reduction of the
measurement result data is crucial to prevent the depletion of the
available (i.e. the affordable) resources. Such a reduction can be
achieved by a reasonable deployment of sampling techniques. Sampling
helps to prevent an exhaustion of resources and to limit the
measurement costs. Examples for applications that benefit from
sampling are given in [DuGG02].
This document concentrates on the deployment of sampling techniques
for packet selection. That means selecting a subset of packets in a
flow. Sampling can be also used to select a set of flows out of all
flows on the link or a set of observation points out of all
observation points in the network. This is not addressed in this
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document. A framework for passive packet measurement and further
packet selection methods can be found in [DuGG02].
2. Terminology
Flow
see definition in [QuZC02]
Metering process
see definition in [QuZC02]
Sample size
The sample size denotes the number of element in the sample.
Sampling (definition from [QuZC02])
Sampling describes the systematic or random selection of a
subset of elements (the sample) out of a set of elements (the
parent population). Usually the purpose of applying sampling
techniques is to estimate a parameter of the parent population
by using only the elements of the subset. Sampling techniques
can be applied for instance to select a subset of packets out
of all packets of a flow or to select a subset of flows out of
all flows on a link. Sampling methods differ in their sampling
strategy (e.g. systematic or random) and in the event that
triggers the selection of an element. The selection of one
packet can for instance be triggered by its arrival time (time-
based sampling), by its position in the flow (count-based
sampling) or by the packet content (content-based sampling)
[QuZC02].
Sampling function
Function that determines whether an element is selected or not
Selection interval
Interval (specified in number of packets or as time duration)
in which all packets are selected.
Selection probability
The probability with which one element is selected as part of
the sample.
3. Deployment of Sampling Techniques for packet selection
The deployment of sampling techniques aims at the provisioning of
information about a specific characteristic of the parent population
at a lower cost than a full census would demand. In order to plan a
suitable sampling strategy it is therefore crucial to determine the
needed type of information and the desired degree of accuracy in
advance.
First of all it is important to know the type of metric that should
be estimated. The metric of interest can range from simple packet
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counts [JePP92] up to the estimation of whole distributions of flow
characteristics [ClPB93].
Secondly, the required accuracy of the information and with this,
the confidence that is aimed at, should be known in advance. For
instance for usage-based accounting the required confidence for the
estimation of packet counters can depend on the monetary value that
corresponds to the transfer of one packet. That means that a higher
confidence could be required for expensive packet flows (e.g.
premium IP service) than for cheaper flows (e.g. best effort). The
accuracy requirements for validating a previously agreed quality can
also vary extremely with the customer demands. These requirements
are usually determined by the service level agreement (SLA).
Sampling is considered as part of the metering process. It can be
applied at different functions of the metering process (e.g. during
packet header capturing, before or after classification, etc.). In
the following we consider a measured IP packets with its observation
point and timestamp as basis elements of the parent population. And
all packets in the flow of interest as the parent population. Please
note that with the IPFIX flow definition the flow of interest can
also include all packets on the link.
The sampling method and the parameters in use must be clearly
communicated to all applications that use the measurement data. Only
with this a correct interpretation of the measurement results can
be ensured.
4. Sampling Methods
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.
4.1 Sampling Algorithm
The sampling algorithm describes the basic process for selection of
samples. In accordance to [AmCa89] and [ClPB93] we define the
following basic sampling processes:
4.1.1 Systematic Sampling
Systematic sampling describes the process of selecting the starting
points and the duration of the selection intervals according to a
deterministic function. This can be for instance the periodic
selection of every n-th element of a trace but also the selection of
all packets that arrive at pre-defined points in time. Even if the
selection process does not follow a periodic function (e.g. if the
time between the sampling intervals varies over time) we consider
this as systematic sampling as long as the selection is
deterministic. The use of systematic sampling always involves the
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risk of biasing the results. If the systematics in the sampling
process resembles 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. In this
context it also has to be considered that there might be systematics
(e.g. periodic repetition of an event) in the observed process which
one might not be aware of in advance.
4.1.2 Random Sampling
Random sampling selects the starting points of the sampling
intervals in accordance to a random process. The selection of
elements are independent experiments. With this, unbiased
estimations can be achieved. In contrast to systematic sampling,
random sampling requires the generation of random numbers. One can
differentiate two methods of random sampling:
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 random numbers and select all packets which have a packet
position equal to one of the random numbers. For this kind of
sampling the sample size is fixed.
Probabilistic sampling (see also [DuGG02])
In probabilistic sampling the decision whether an element is
selected or not is made in accordance to a pre-defined selection
probability. An example would be to flip a coin for each packet and
select all packets for which the coin showed the head. For this kind
of sampling the sample size can vary for different trials. The
selection probability is not necessarily the same for each packet
and can depend on other parameters (e.g. the packet content)
[DuGG02].
4.1.3 Stratified Sampling
The basic idea behind stratified sampling is to increase the
estimation accuracy by using a-priori information. The a-priori
information is used to perform an intelligent grouping of the
elements of the parent population. With this a higher estimation
accuracy can be achieved with the same sample size.
Stratified sampling divides the sampling process into multiple
steps. First, the elements of the parent population are grouped into
subsets in accordance to a given characteristic. This grouping can
be done in multiple steps. Then samples are taken from each subset.
The stronger the correlation between the characteristic used to
divide the parent population 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
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instance if the dividing characteristic were equal to the
investigated characteristic, each element of the sub-group would be
a perfect representative of that characteristic. In this case it
would be sufficient to take one arbitrary element out of each
subgroup to get the actual distribution of the characteristic in the
parent population. Therefore stratified sampling can reduce the
costs for the sampling process (i.e. the number of samples needed to
achieve a given level of confidence).
4.2 Sampling Frequency and Interval-Length
According to [AmCa89] and [ClPB93] we differentiate sampling
techniques by the event that triggers the sampling process. The
trigger determines what kind of event starts and stops the sampling
intervals. With this the sampling frequency and the length of the
sampling interval (measured in packets or time) is determined. It is
also possible to combine start and stop triggers of different types
(e.g. start a 10 s measurement interval every n-th packet).
Nevertheless, due to the unknown relation between number of packets
and duration of an interval this can lead to unexpected overlapping
of sampling intervals. We distinguish the following techniques:
4.2.1 Count-based Trigger
With this method the packet count triggers the start and stop of a
sampling interval. One example is the systematic sampling of every
n-th packet of a specific type. For count-based sampling it is
necessary to integrate a packet counter into the meter. Since non-
intrusive measurements are based on the traffic in the network only,
the time that it takes until the n packets of a specific type are
seen by the probe is unknown. This means the duration of the
sampling process is undetermined (and can be infinite) if the
sampling goal requires a minimum sampling size (number of packets).
4.2.2 Time-based Trigger
In time-based sampling the arrival time of a packet at the meter
determines whether this packet is captured or not. One example is to
capture packets every 30 seconds. If the stop trigger is also a
point in time the sampling interval length is given as the time
duration between this two points. Since it is unknown how many
packets arrive in a specific time interval the number of packets
captured with this technique is unknown (and can be zero). This has
to be taken into account if a minimum sampling size is required.
4.2.3 Packet-content-based Trigger
With this method the content (or parts of the content) of the packet
itself (header, payload or both) triggers the sampling process. This
can be achieved by direct comparison of parts of the packet with a
reference pattern [CoGi98] or by matching the result of a function
performed on packet content [DuGr00].
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5. Sampling Parameters
The decision whether to select a packet or not is based on a
function which takes packet properties and sampling parameters as
input. The sampling parameters usually remain the same for the
sampling process and are pre-defined by the administrator. A special
case are sampling parameters that depend on packet properties (e.g.
selection probability dependent on packet content). In such cases
only the function which describes the dependency is fixed in
advance. Packet properties are examined per packet and are only
available after the packet has arrived at the meter.
sampling parameters
|
V
+-------------------+
packet properties | sampling function |
------------------->| |----> selected/not selected
+-------------------+
Selection decision = f(sampling parameters, packet properties)
Packet properties are: packet position, arrival time, packet content
(header fields, parts of payload) and observation point.
Which packet properties are used as input for the sampling function
is determined by the used sampling algorithm. For count based
sampling the packet position is used as input. For time-based
sampling the arrival time and for content-based sampling (parts of)
the packet content (e.g. header fields). It is also possible, that
the algorithm differs with regard to the observation point on which
the packet was observed. The sampling parameters differ for the
different sampling technique.
5.1 Parameters for systematic sampling
For systematic sampling the deterministic function which is used for
the packet selection needs to be given. For periodic sampling the
start of the first selection interval, the length of the selection
interval (given in number of packets or as time duration) and the
spacing between selection intervals needs to be specified.
<-- interval length = 7 --> <-- spacing = 5 -->
Paket position: 1 2 3 4 5 6 7 8 9 10 11 12 13..
In sample: 1,2,3,4,5,6,7, 13,...
Selecting every x-th packet would be a special case with selection
interval=1 and spacing=x-1.
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5.2 Parameters for random sampling
For random n-out-of-N sampling only the sample size n needs to be
specified. This can be done either as an absolute number or as
fraction of the parent population n/N.
For probabilistic sampling the selection probability p needs to be
specified. If the selection probability depends on other parameters
(e.g. packet content), the function that expresses this dependency
has to be specified.
5.3 Parameters for stratified sampling
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. For the sampling scheme within the
subgroups the parameters have to be specified as described above.
6. Security Considerations
Security threats can occur if the configuration of sampling
parameters or the communication of sampling parameters to the
application is corrupted. This document only describes sampling
schemes that can be used for packet selection. It neither describes
a mechanism how those parameters are configured nor how these
parameters are communicated to the application. Therefore the
security threats that originate from this kind of communication
cannot be assessed with the information given in this document.
7. References
[AmCa89] Paul D. Amer, Lillian N. Cassel: Management of Sampled
Real-Time Network Measurements, 14th Conference on Local
Computer Networks, October 1989, Minneapolis, pages 62-
68, IEEE, 1989
[ClPB93] K.C. Claffy, George C Polyzos, Hans-Werner Braun:
Application of Sampling Methodologies to Network Traffic
Characterization, Proceedings of ACM SIGCOMM'93, San
Francisco, CA, USA, September 13 - 17, 1993
[CoGi98] I. Cozzani, S. Giordano: Traffic Sampling Methods for
end-to-end QoS Evaluation in Large Heterogeneous
Networks. Computer Networks and ISDN Systems, 30 (16-
18), September 1998.
[DuGG02] Nick Duffield, Albert Greenberg, Matthias Grossglauser,
Jennifer Rexford: A Framework for Passive Packet
Measurement, Internet Draft draft-duffield-framework-
papame-01, work in progress, February 2002
[DuGr00] Nick Duffield, Matthias Grossglauser: Trajectory
Sampling for Direct Traffic Observation, Proceedings of
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ACM SIGCOMM 2000, Stockholm, Sweden, August 28 -
September 1, 2000.
[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,
h
ttp://www.hpl.hp.com/techreports/92/HPL-92-35.htm
l
[QuZC02] J. Quittek, T. Zseby, B. Claise, S. Zander, G. Carle,
K.C. Norseth: Requirements for IP Flow Information
Export, Internet Draft <draft-ietf-ipfix-reqs-05.txt>,
work in progress, August 2002
[Zseb02] Tanja Zseby: Deployment of Sampling Methods for SLA
Validation with Non-Intrusive Measurements, Proceedings
of Passive and Active Measurement Workshop (PAM 2002),
Fort Collins, CO, USA, March 25-26, 2002
8. Author's Addresses
Tanja Zseby
Fraunhofer Institute for Open Communication Systems
Kaiserin-Augusta-Allee 31
10589 Berlin
Germany
Phone: +49-30-34 63 7153
Fax: +49-30-34 53 8153
Email: zseby@fokus.fhg.de
9. Full Copyright Statement
Copyright (C) The Internet Society (2002). All Rights Reserved. This
document and translations of it may be copied and furnished to
others, and derivative works that comment on or otherwise explain it
or assist in its implementation may be prepared, copied, published
and distributed, in whole or in part, without restriction of any
kind, provided that the above copyright notice and this paragraph
are included on all such copies and derivative works. However, this
document itself may not be modified in any way, such as by removing
the copyright notice or references to the Internet Society or other
Internet organizations, except as needed for the purpose of
developing Internet standards in which case the procedures for
copyrights defined in the Internet Standards process must be
followed, or as required to translate it into languages other than
English.
The limited permissions granted above are perpetual and will not be
revoked by the Internet Society or its successors or assigns.
This document and the information contained herein is provided on an
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"AS IS" basis and THE INTERNET SOCIETY AND THE INTERNET ENGINEERING
TASK FORCE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING
BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION
HEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED WARRANTIES OF
MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
Zseby Expires February 2003 [Page 10]