1 draft-duffield-ippm-burst-loss-metrics-01.txt nick duffield, al morton, at&t joel sommers,...
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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
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