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draft-duffield-ippm-burst-loss-metrics-01.txt

Nick Duffield, Al Morton, AT&TJoel Sommers, Colgate University

IETF 76, Hiroshima, Japan 11/10/2009

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Agenda

History of the draft

One page summary of draft

Mailing list comments and discussion

Related activity

Conclusions

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History of draft-duffield-ippm-burst-loss-metrics

Aim: standardize measurement of loss episodes [SBDR08]

Initial presentations IETF 72, 73

-00 individual draft published prior to IETF 74

IPR disclosures for -00 draft completed April 2009

-01 draft published July 2009

Open question: should draft be adopted as WG item?

Some comments and questions on draft on the IPPM mailing list Thanks for comments; more please!

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A one page summary of the draft

Fact: packets in a flow are not generally loss independently

Motivation: metrics of temporal structure of packet loss

Target use: SLAs, application requirements (e.g VoIP)

Object of study: loss episodes (of consecutively loss packets)

Metrics: average duration and frequency of loss episodes

Probing: bi-packet probes, sent as discrete Poisson stream

Analysis: metrics depend only on frequencies probe outcomes 4 possible outcomes (0,0), (0,1), (1,1), (1,0) where 1 = lost, 0 =

not lost

Summary: extension of RFC 2680 to case of correlated loss

X X X X

XXXXFrequent small glitches vs. local burst (at same average loss rate)

(0,0) (0,1) (1,1) (0,0)

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Mailing list comments and discussion

Should metrics be loss episodes average or general burstiness?

What is the relation to Gilbert model?

Should metrics be time based or count based?

Need for clarification of role of selection function

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Metrics: Loss episode averages or burstiness?

Structure of loss episodes is more complex that average length

Multipacket statistics? (Prob[episode has n packets], n = 1,2,3,…)

Correlations between lengths of episodes, gaps between episodes?

Questions/Issues:

What is added utility of multipacket statistics over averages?

Metric statistical accuracy decreases with number of packets n

Authors’ Recommendation

Retain only loss episode averages (simple extension of RFC 2680)

Defer multipacket loss statistics as separate WG item if interest

NB: averaging metrics do not need to sample full loss episodes

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What is relation to Gilbert model?

In parametric terms, the Gilbert model is more complex Gilbert model has 4 parameters: Good/Bad state lifetimes/loss

rate

Two independent loss episode metrics (average duration, frequency)

Metrics are purely empirical, interpreted independent of model

Metrics do not aim to estimate parameters of any model

Authors’ Recommendation: expand draft with applicability section

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Time based or count based episode metrics?

Some well known burstiness metric are based on packet counts IDC: index of dispersion on counts

Loss episode metrics based on time (average duration etc) Easier to compare directly with application requirements

Probe rate and traffic rate generally different

Authors’ Recommendation: Retain time-basis

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What is the role of selection function?

Selection function is a general formulation of a way to specify which packet are used for probing (see RFC 3393)

Examples: Specifying how discrete Poisson b-packet probes are to be

selected

Potential use to specify selection mechanism for background traffic to be co-opted as probes.

Authors’ Recommendation: Expand explanation of selection function in draft.

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Related Activity

ITU Activity contributed to ITU-T SG 12 Question 17 on packet performance.

independent implementation of same loss episode metrics • Special case: unsampled counts of 4 bi-packet outcomes

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Authors’ Conclusions

Mailing list discussion has been helpful and constructive; thanks!

Points raised appear to request clarifications and elaboration, rather than raising fundamental objections to metrics or methods

Authors will update accordingly in next draft version