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The IETF has developed a one way packet loss metric that measures the loss rate on a Poisson probe stream between two hosts. However, the impact of packet loss on applications is in general sensitive not just to the average loss rate, but also to the way in which packet losses are distributed in loss episodes (i.e., maximal sets of consecutively lost probe packets). This draft defines one-way packet loss episode metrics, specifically the frequency and average duration of loss episodes, and a probing methodology under which the loss episode metrics are to be measured.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 (Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” March 1997.) [RFC2119]
This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at http://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as “work in progress.”
This Internet-Draft will expire on July 4, 2011.
Copyright (c) 2010 IETF Trust and the persons identified as the document authors. All rights reserved.
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1.
Introduction
1.1.
Background and Motivation
1.2.
Loss Episode Metrics and Bi-Packet Probes
1.3.
Outline and Contents
2.
Singleton Definition for Type-P-One-way Bi-Packet Loss
2.1.
Metric Name
2.2.
Metric Parameters
2.3.
Metric Units
2.4.
Metric Definition
2.5.
Discussion
2.6.
Methodologies
2.7.
Errors and Uncertainties
2.8.
Reporting the Metric
3.
General Definition of samples for Type-P-One-way-Bi-Packet-Loss
3.1.
Metric Name
3.2.
Metric Parameters
3.3.
Metric Units
3.4.
Metric Definition
3.5.
Discussion
3.6.
Methodologies
3.7.
Errors and Uncertainties
3.8.
Reporting the Metric
4.
An active probing methodology for Bi-Packet Loss
4.1.
Metric Name
4.2.
Metric Parameters
4.3.
Metric Units
4.4.
Metric Definition
4.5.
Discussion
4.6.
Methodologies
4.7.
Errors and Uncertainties
4.8.
Reporting the Metric
5.
Loss Epsiode Proto-Metrics
5.1.
Loss-Pair-Counts
5.2.
Bi-Packet-Loss-Ratio
5.3.
Bi-Packet-Loss-Episode-Duration-Number
5.4.
Bi-Packet-Loss-Episode-Frequency-Number
6.
Loss Episode Metrics derived from Bi-Packet Loss Probing
6.1.
Geometric Stream: Loss Ratio
6.1.1.
Metric Name
6.1.2.
Metric Parameters
6.1.3.
Metric Units
6.1.4.
Metric Definition
6.1.5.
Discussion
6.1.6.
Methodologies
6.1.7.
Errors and Uncertainties
6.1.8.
Reporting the Metric
6.2.
Geometric Steam: Loss Episode Duration
6.2.1.
Metric Name
6.2.2.
Metric Parameters
6.2.3.
Metric Units
6.2.4.
Metric Definition
6.2.5.
Discussion
6.2.6.
Methodologies
6.2.7.
Errors and Uncertainties
6.2.8.
Reporting the Metric
6.3.
Geometric Stream: Loss Episode Frequency
6.3.1.
Metric Name
6.3.2.
Metric Parameters
6.3.3.
Metric Units
6.3.4.
Metric Definition
6.3.5.
Discussion
6.3.6.
Methodologies
6.3.7.
Errors and Uncertainties
6.3.8.
Reporting the Metric
7.
Applicability of Loss Episode Metrics
7.1.
Relation to Gilbert Model
8.
IPR Considerations
9.
Security Considerations
10.
IANA Considerations
11.
Acknowledgements
12.
References
12.1.
Normative References
12.2.
Informative References
§
Authors' Addresses
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Packet loss in the Internet is a complex phenomenon due to the bursty nature of traffic and congestion processes, influenced by both end-users and applications, and the operation of transport protocols such as TCP. For these reasons, the simplest model of packet loss--the single parameter Bernoulli (independent) loss model--does not represent the complexity of packet loss over periods of time. Correspondingly, a single loss metric--the average packet loss ratio over some period of time--arising, e.g., from a stream of Poisson probes as in [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) is not sufficient to determine the effect of packet loss on traffic in general.
Moving beyond single parameter loss models, Markovian and Markov-modulated loss models involving transitions between a good and bad state, each with an associated loss rate, have been proposed by Gilbert and more generally by Elliot. In principle, Markovian models can be formulated over state spaces involving patterns of loss of any desired number of packets. However further increase in the size of the state space makes such models cumbersome both for parameter estimation (accuracy decreases) and prediction in practice (due to computational complexity and sensitivity to parameter inaccuracy). In general, the relevance and importance of particular models can change in time, e.g. in response to the advent of new applications and services. For this reason we are drawn to empirical metrics that do not depend on a particular model for their interpretation.
An empirical measure of packet loss complexity, the index of dispersion of counts (IDC), comprise, for each t >0, the ratio v(t) \ a(t) of the variance v(t) and average a(t) of the number of losses over successive measurement windows of a duration t. However, a full characterization of packet loss over time requires specification of the IDC for each window size t>0.
In the standards arena, loss pattern sample metrics are defined in [RFC3357] (Koodli, R. and R. Ravikanth, “One-way Loss Pattern Sample Metrics,” August 2002.). Following the Gilbert-Elliot model, burst metrics specific for VoIP that characterize complete episodes of lost, transmitted and discarded packets are defined in [RFC3611] (Friedman, T., Caceres, R., and A. Clark, “RTP Control Protocol Extended Reports (RTCP XR),” November 2003.)
All these considerations motivate formulating empirical metrics of one-way packet loss that provide the simplest generalization of the successful [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) that can capture deviations from independent packet loss in a robust model-independent manner, and, to define efficient measurement methodologies for these metrics.
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The losses experienced by the packet stream can be viewed as occurring in loss episodes, i.e., maximal set of consecutively lost packets. This memo describes one-way loss episode metrics: their frequency and average duration. Although the average loss ratio can be expressed in terms of these quantities, they go further in characterizing the statistics of the patterns of packet loss within the stream of probes. This is useful information in understanding the effect of packet losses on application performance, since different applications can have different sensitivities to patterns of loss, being sensitive not only to the long term average loss rate, but how losses are distributed in time. As an example: MPEG video traffic may be sensitive to loss involving the I-frame in a group of pictures, but further losses within an episode of sufficiently short duration have no further impact; the damage is already done.
The loss episode metrics presented here represent have the following useful properties:
The draft also describes a probing methodology under which loss episode metrics are to be measured. The methodology comprises sending probe packets in pairs, where packets within each probe pair have a fixed separation, and the time between pairs takes the form of a geometric distributed number multiplied by the same separation. This can be regarded a generalization of Poisson probing where the probes are pairs rather than single packets as in [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.), and also of geometric probing described in [RFC2330] (Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, “Framework for IP Performance Metrics,” May 1998.). However, it should be distinguished from back to back packet pairs whose change in separation on traversing a link is used to probe bandwidth. In this draft, the separation between the packets in a pair is the temporal resolution at which different loss episodes are to be distinguished. One key feature of this methodology is its efficiency: it estimates the average length of loss episodes without directly measuring the complete episodes themselves. Instead, this information is encoded in the observed relative frequencies of the 4 possible outcomes arising from the loss or successful transmission of each of the two packets of the probe pairs. This is distinct from the approach of [RFC3611] (Friedman, T., Caceres, R., and A. Clark, “RTP Control Protocol Extended Reports (RTCP XR),” November 2003.) that reports on directly measured episodes.
The metrics defined in this memo are "derived metrics", according to Section 6.1 of [RFC2330] (Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, “Framework for IP Performance Metrics,” May 1998.) the IPPM framework. They are based on the singleton loss metric defined in Section 2 of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) .
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Type-P-One-way-Bi-Packet-Loss
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A Loss Pair is pair (l1, l2) where each of l1 and l2 is a binary value 0 or 1, where 0 signifies successful transmission of a packet and 1 signifies loss.
The metric unit for Type-P-One-way-Bi-Packet-Loss takes is a Loss Pair
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The purpose of the selection function is to specify exactly which packets are to be used for measurement. The notion is taken from Section 2.5 of [RFC3393] (Demichelis, C. and P. Chimento, “IP Packet Delay Variation Metric for IP Performance Metrics (IPPM),” November 2002.), where examples are discussed.
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The methodologies related to the Type-P-One-way-Packet-Loss metric in Section 2.6 of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) are similar for the Type-P-One-way-Bi-Packet-Loss metric described above. In particular, the methodologies described in RFC 2680 apply to both packets of the pair.
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Sources of error for the Type-P-One-way-Packet-Loss metric in Section 2.7 of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) apply to each packet of the pair for the Type-P-One-way-Bi-Packet-Loss metric.
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Refer to Section 2.8 of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.).
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Given the singleton metric for Type-P-One-way-Bi-Packet-Loss, we now define examples of samples of singletons. The basic idea is as follows. We first specify a set of times T1 < T2 <...<Tn, each of which acts as the first time of a packet pair for a single Type-P-One-way-Bi-Packet-Loss measurement. This results is a set of n metric values of Type-P-One-way-Bi-Packet-Loss.
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Type-P-One-way-Bi-Packet-Loss-Stream
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A set L1,L2,...,Ln of loss pairs
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Each loss pair Li for i-1,....n is the Type-P-One-way-Bi-Packet-Loss with parameters (Src, Dst, Ti1, Ti2, Fi, P) where Fi is the restriction of the selection function F to the packet pair at time Ti1, Ti2.
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The metric definition of Type-P-One-way-Bi-Packet-Loss-Stream is sufficiently general to describe the case where packets are sampled from a pre-existing stream. This is useful in the case that there is a general purpose measurement stream setup between two hosts, and we which to select a substream from it for the purposes of loss episode measurement. In the next section we specialize this somewhat to more concretely describe a purpose built packet stream for loss episode measurement.
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This section specializes the preceding section for an active probing methodology. The basic idea is a follows. We set up a sequence of evenly spaced times T1 < T2 < ... < Tn. Each time Ti is potentially the first packet time for a packet pair measurement. We make an independent random decision at each time, whether to initiate such a measurement. Hence the interval count between successive times at which a pair is initiated follows a geometric distribution. We also specify that the spacing between successive times Ti is the same as the spacing between packets in a given pair. Thus if pairs happen to be launched at the successive times Ti T(i+1), the second packet of the first pair is actually used as the first packet of the second pair.
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream
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A set of Loss Pairs L1, L2, ..., Lm for some m <= n
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for each i = 0, 1, ..., n-1 we form the potential measurement time Ti = T + i * d. With probability q, a packet pair measurement is launched at Ti, resulting in a Type-P-One-way-Bi-Packet-Loss with parameters (Src, Dst, Ti, Ti+1, Fi, P) where Fi is the restriction of the selection function F to the packet pair at times Ti, Ti+1. L1, L2,...Lm are the resulting Loss Pairs; m can be less than n since not all time Ti have an associated measurement.
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The above definition of Type-P-One-way-Bi-Packet-Loss-Geometric-Stream is equivalent to using Type-P-One-way-Bi-Packet-Loss-Stream with an appropriate statistical definition of the selection function F.
The number m of loss pairs in the metric can be less than the number of potential measurement instants because not all instants may generate a probe when the launch probability q is strictly less than 1.
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The methodologies follow from:
The issue of choosing an appropriate time spacing (e.g., one that is matched to expected characteristics of loss episodes) is outside the scope of this document.
Note that as with any active measurement methodology, consideration must be made to handle out-of-order arrival of packets; see also Section 3.6. of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.).
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In addition to sources of errors and uncertainties related to methodologies for measuring the singleton Type-P-One-way-Bi-Packet-Loss metric, a key source of error when emitting packets for Bi-Packet Loss relates to resource limits on the host used to send the packets. In particular, the choice of T0, the choice of the time spacing, and the choice of the launch probability results in a schedule for sending packets. Insufficient CPU resources on the sending host may result in an inability to send packets according to schedule. Note that the choice of time spacing directly affects the ability of the host CPU to meet the required schedule (e.g., consider a 100 microsecond spacing versus a 100 millisecond spacing).
For other considerations, refer to Section 3.7. [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.).
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Refer to Section 3.8. of [RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.).
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This section describes four generic proto-metric quantities associated with an arbitrary set of loss pairs. These are the Loss-Pair-Counts, Bi-Packet-Loss-Ratio, Bi-Packet-Loss-Episode-Duration-Number, Bi-Packet-Loss-Episode-Frequency-Number. Specific loss episode metrics can then be constructed when these proto metrics take as their input, sets of loss pairs samples generated by the Type-P-One-way-Bi-Packet-Loss-Stream and Type-P-One-way-Bi-Packet-Loss-Geometric Stream. The second of these is described in Section 4 (An active probing methodology for Bi-Packet Loss). It is not expected that these proto-metrics would be reported themselves. Rather they are intermediate quantities in the production of the final metrics of Section 6 below, and could be rolled up into them in implementations. The metrics report loss episode durations and frequencies in terms of packet counts, since they do not depend on the actual time between probe packets. The final metrics of Section 6 incorporate timescales and yield durations in seconds, and frequencies as per second.
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Loss-Pair-Counts are the absolute frequencies of the 4 types of loss pair outcome in a sample. More precisely, the Loss-Pair-Counts associated with a set of loss pairs L1,,,,Ln are the numbers N(i,j) of such loss pairs that take each possible value (i,j) in the set ( (0,0), (0,1), (1,0), (1,1)).
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The Bi-Packet-loss-ratio associated with a set of n loss pairs L1,,,,Ln is defined in terms of their Loss-Pair-Counts by the quantity (N(1,0) +N(1,1))/n.
Note this is formally equivalent to the loss metric Type-P-One-way-Packet-Loss-Average from[RFC2680] (Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” September 1999.) since it averages single packet losses.
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The Bi-Packet-Loss-Episode-Duration-Number associated with a set of n loss pairs L1,,,,Ln is defined in terms of their Loss-Pair-Counts in the following cases:
Note N(0,1) + N(1,0) is zero if there are no transitions between loss and no-loss outcomes.
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The Bi-Packet-Loss-Episode-Frequency-Number associated with a set of n loss pairs L1,,,,Ln is defined in terms of their Loss-Pair-Counts as Bi-Packet-Loss-Ratio / Bi-Packet-Loss-Episode-Duration-Number, when this can be defined, specifically, it is:
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Metrics for the time frequency and time duration of loss episodes are now defined as functions of set of n loss pairs L1,....,Ln. Although a loss episode is defined as a maximal set of successive lost packets, the loss episode metrics are not defined directly in terms of the sequential patterns of packet loss exhibited by loss pairs. This is because samples, including Type-P-One-way-Bi-Packet-Loss-Geometric-Stream, generally do not report all lost packets in each episode. Instead, the metrics are defined as functions of the Loss-Pair-Counts of the sample, for reasons that are now described.
Consider an idealized Type-P-One-way-Bi-Packet-Loss-Geometric-Stream sample in which the launch probability q =1. It is shown in [SBDR08] (IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008.) that the average number of packets in a loss episode of this ideal sample is exactly the Bi-Packet-Loss-Episode-Duration derived from its set of loss pairs. Note this computation makes no reference to the position of lost packet in the sequence of probes.
A general Type-P-One-way-Bi-Packet-Loss-Geometric-Stream sample with launch probability q < 1, independently samples, with probability q, each loss pair of an idealized sample. On average, the Loss-Pair-Counts (if normalized by the total number of pairs) will be the same as in the idealized sample. The loss episode metrics in the general case are thus estimators of those for the idealized case; the statistical properties of this estimation, including a derivation of the estimation variance, is provided in [SBDR08] (IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008.).
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Ratio
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A number in the interval [0,1]
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The result obtained by computing the Bi-Packet-Loss-Ratio over a Type-P-One-way-Bi-Packet-Loss-Geometric-Stream sample with the metric parameters.
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Ratio estimates the fraction of packets lost from the geometric stream of Bi-Packet probes.
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Refer to Section 4.6 (Methodologies)
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Because Type-P-One-way-Bi-Packet-Loss-Geometric-Stream is sampled in general (when the launch probability q <1) the metrics described in this Section can be regarded as statistical estimators of the corresponding idealized version corresponding to q = 1. Estimation variance as it applies to Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Loss-Ratio is described in [SBDR08] (IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008.).
For other issues refer to Section 4.7 (Errors and Uncertainties)
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Refer to Section 4.8 (Reporting the Metric)
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Duration
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A non-negative number of seconds.
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The result obtained by computing the Bi-Packet-Loss-Episode-Duration-Number over a Type-P-One-way-Bi-Packet-Loss-Geometric-Stream sample with the metric parameters, then multiplying the result by the launch spacing parameter d.
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Duration estimates the average duration of a loss episode, measured in seconds. The duration measured in packets is obtained by dividing the metric value by the packet launch spacing parameter d.
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Refer to Section 4.6 (Methodologies)
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Because Type-P-One-way-Bi-Packet-Loss-Geometric-Stream is sampled in general (when the launch probability q <1) the metrics described in this Section can be regarded as statistical estimators of the corresponding idealized version corresponding to q = 1. Estimation variance as it applies to Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Duration is described in [SBDR08] (IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008.).
For other issues refer to Section 4.7 (Errors and Uncertainties)
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Refer to Section 4.8 (Reporting the Metric)
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Frequency
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A positive number.
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The result obtained by computing the Bi-Packet-Loss-Episode-Frequency over a Type-P-One-way-Bi-Packet-Loss-Geometric-Stream sample with the metric parameters, then dividing he result by the launch spacing parameter d.
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Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Frequency estimates the average frequency per unit time with which loss episodes start (or finish). The frequency relative to the count of potential probe launches is obtained by multiplying the metric value by the packet launch spacing parameter d.
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Refer toSection 4.6 (Methodologies)
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Because Type-P-One-way-Bi-Packet-Loss-Geometric-Stream is sampled in general (when the launch probability q <1) the metrics described in this Section can be regarded as statistical estimators of the corresponding idealized version corresponding to q = 1. Estimation variance as it applies to Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Frequency is described in [SBDR08] (IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008.).
For other issues refer to Section 4.7 (Errors and Uncertainties)
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Refer to Section 4.8 (Reporting the Metric)
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The general Gilbert-Elliot model is a discrete time Markov chain over two states, Good (g) and Bad (b), each with its own independent packet loss rate. In the simplest case, the Good loss rate is 0 while the Bad loss rate is 1. Correspondingly, there are two independent parameters, the Markov transition probabilities P(g|b) = 1- P(b|b) and P(b|g) = 1- P(g|g), where P(i|j) is the probability to transition from state j and step n to state i at step n+1. With these parameters, the fraction of steps spent in the bad state is P(b|g)/(P(b|g) + P(g|b)) while the average duration of a sojourn in the bad state is 1/P(g|b) steps.
Now identify the steps of the Markov chain with the possible sending times of packets for a Type-P-One-way-Bi-Packet-Loss-Geometric-Stream with launch spacing d. Suppose the loss episode metrics Type-P-One-way-Bi-Packet-Loss-Geometric-Stream-Ratio and ype-P-One-way-Bi-Packet-Loss-Geometric-Stream-Episode-Duration take the values r and m respectively. Then from the discussion in Section 6.2.5 the following can be equated:
r = P(b|g)/(P(b|g) + P(g|b)) and m/d = 1/P(g|b).
These relationships can be inverted in order to recover the Gilbert model parameters:
P(g|b) = d/m and P(b|g)=d/m/(1/r - 1)
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IPR disclosures concerning some of the material covered in this draft has been made to the IETF: see https://datatracker.ietf.org/ipr/1009/ , https://datatracker.ietf.org/ipr/1010/ , and https://datatracker.ietf.org/ipr/1126/
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Conducting Internet measurements raises both security and privacy concerns. This memo does not specify an implementation of the metrics, so it does not directly affect the security of the Internet nor of applications which run on the Internet. However,implementations of these metrics must be mindful of security and privacy concerns.
There are two types of security concerns: potential harm caused by the measurements, and potential harm to the measurements. The measurements could cause harm because they are active, and inject packets into the network. The measurement parameters MUST be carefully selected so that the measurements inject trivial amounts of additional traffic into the networks they measure. If they inject "too much" traffic, they can skew the results of the measurement, and in extreme cases cause congestion and denial of service. The measurements themselves could be harmed by routers giving measurement traffic a different priority than "normal" traffic, or by an attacker injecting artificial measurement traffic. If routers can recognize measurement traffic and treat it separately, the measurements may not reflect actual user traffic. If an attacker injects artificial traffic that is accepted as legitimate, the loss rate will be artificially lowered. Therefore, the measurement methodologies SHOULD include appropriate techniques to reduce the probability that measurement traffic can be distinguished from "normal" traffic. Authentication techniques, such as digital signatures, may be used where appropriate to guard against injected traffic attacks. The privacy concerns of network measurement are limited by the active measurements described in this memo: they involve no release of user data.
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[RFC2119] | Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” BCP 14, RFC 2119, March 1997 (TXT, HTML, XML). |
[RFC2680] | Almes, G., Kalidindi, S., and M. Zekauskas, “A One-way Packet Loss Metric for IPPM,” RFC 2680, September 1999 (TXT). |
[RFC3393] | Demichelis, C. and P. Chimento, “IP Packet Delay Variation Metric for IP Performance Metrics (IPPM),” RFC 3393, November 2002 (TXT). |
[RFC3432] | Raisanen, V., Grotefeld, G., and A. Morton, “Network performance measurement with periodic streams,” RFC 3432, November 2002 (TXT). |
[RFC3611] | Friedman, T., Caceres, R., and A. Clark, “RTP Control Protocol Extended Reports (RTCP XR),” RFC 3611, November 2003 (TXT). |
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[RFC2330] | Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, “Framework for IP Performance Metrics,” RFC 2330, May 1998 (TXT, HTML, XML). |
[RFC3357] | Koodli, R. and R. Ravikanth, “One-way Loss Pattern Sample Metrics,” RFC 3357, August 2002 (TXT). |
[SBDR08] | IEEE/ACM Transactions on Networking, 16(2): 307-320, “A Geometric Approach to Improving Active Packet Loss Measurement,” 2008. |
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Nick Duffield | |
AT&T Labs-Research | |
180 Park Avenue | |
Florham Park, NJ 07932 | |
USA | |
Phone: | +1 973 360 8726 |
Fax: | +1 973 360 8871 |
Email: | duffield@research.att.com |
URI: | http://www.research.att.com/people/Duffield_Nicholas_G |
Al Morton | |
AT&T Labs | |
200 Laurel Avenue South | |
Middletown,, NJ 07748 | |
USA | |
Phone: | +1 732 420 1571 |
Fax: | +1 732 368 1192 |
Email: | acmorton@att.com |
URI: | http://home.comcast.net/~acmacm/ |
Joel Sommers | |
Colgate University | |
304 McGregory Hall | |
Hamilton, NY 13346 | |
USA | |
Phone: | +1 315 228 7587 |
Fax: | |
Email: | jsommers@colgate.edu |
URI: | http://cs.colgate.edu/faculty/jsommers |