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Locating Congested Segments over the Internet Based on Multiple End-to-End Path Measurements Shigehiro Ano * Atsuo Tachibana * Toru Hasegawa * Masato Tsuru.

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Presentation on theme: "Locating Congested Segments over the Internet Based on Multiple End-to-End Path Measurements Shigehiro Ano * Atsuo Tachibana * Toru Hasegawa * Masato Tsuru."— Presentation transcript:

1 Locating Congested Segments over the Internet Based on Multiple End-to-End Path Measurements Shigehiro Ano * Atsuo Tachibana * Toru Hasegawa * Masato Tsuru ** Yuji Oie ** * KDDI R&D Laboratories Inc. ** Kyusyu Institute of Technology

2 2 Contents Introduction Congestion occur in the Internet (social infrastructure) Inference Methods Inference by Packet Loss Measurement Inference by Packet Delay Variation Measurement Measurement Infrastructure & Conditions Analysis and results Measurement Snapshots Analysis Based on the Internet Location Correlation between Each Inference Methods (Loss Rate & Delay Variation) Conclusion

3 Introduction Internet is serving as a communication infrastructure. But, congestion is very likely to occur in the Internet (best effort). Network administrator must take actions to mitigate the congestion. route management traffic engineering How can we locate congested segments ? SNMP not scalable networks are operated independently (in the Internet) Bottleneck identification tool difficult to use all over the networks continuously due to extraordinary test packts We propose Network tomographic approach based on end-to-end packet loss & delay measurement with low rate probing packet

4 4 Y j : non-loss rate on P X i : non-loss rate on S i An Inference Method Using Loss Measurement (1) Detect path performance degradation characterize path performance Y j h good, Y j l bad, otherwise medium Infer bad segments rule(1) P i is ood but P j is bad S j is bad (X i h) if l h 2 < h, this rule is held rule(2) both P i and P j are bad S 3 is likely bad (X 3 h) Assumptions X 1, X 2 and X 3 are independent. X 1, X 2 and X 3 are likely to be good bad is uncommon X 3 (along P 1 ) and X 3 (along P 2 ) are nearly same. We adopt 0.99 and as l and h as l and h

5 5 Rule(1) Rule(2) mapping table An example of the path topology An Inference Method Using Loss Measurement (2)

6 6 OK 0 non-congested path OK congested paths Procedure (1)Assign each monitored path to its own specific cluster (2)Calculate the distance for each pair of two different paths (3)Merge the closest pair of clusters (4)Calculate the distance between the new merged cluster and each of the old cluster Adopt Ward s Method [1] (5)Repeat (3) and (4) until all paths are clustered into a single cluster (6)Determine a partition by cutting the dendrogram recursively (7)Infer bad segments using rule (2) of the inference method using loss measurement heavy congestion may hide mild congestion Recursive Cutting [1] Ward, J. H, "Hierarchical Grouping to Optimize an Objective Function." Journal of the American Statistical Association, 58, pp , An Inference Method Using Delay Measurement (1) 10ms is the threshold to detect congestion. 10ms is the threshold to detect congestion.

7 7 distance Define distance based on the non-similarity of the time series o f packet delay variation Utilizing TWD (Time Warping Distance) [2] - TWD tries to find the optimal alignment between two time series the sum of differences resulting from the alignment is minimized. the larger difference between the two packet delay variations pi and qj d(w k ) pseudo packet pair An Inference Method Using Delay Measurement (2)

8 8 Measurement Infrastructure & Conditions Location B Location A about 1000 km Subscribing to 3 ISP networks via FTTH access NW (max.100Mbps) at each location Measure non-loss packet rate every 15 second and 99%tile of delay variation every 5 second actively on 30 paths. Test packets: 64byte UDP, uniformed distributed interval: 10-90ms (about 10Kbps) traceroute is issued every 1 minute

9 9 about 90% (67202 / periods) of the clustering results were consistent with route information packet loss rate from ISP1 at Location A to ISP2 at Location B The paths states that are classified into the same cluster are synchronized Our method infer S1 is bad. S2 and S5 does not become bad simultaneously. Result (1) Loss Rate & Delay Variation Snapshots Loss Rate Snapshot Dealy Variation (ms) Delay Variation Snapshot

10 10 Result (2) Analysis Based on Location Congestion tends to occur on specific segments The total number of 5-second periods in which 5 segments are cong ested represents about 81% of the total number of congested results Our method should be assistance to ISPs by helping them to allocate their investment resources efficiently delay variation loss rate

11 11 Result(3) Correlation Between the Inference Results Based on Loss Rate and Delay Variation Inference based on loss rate congested segment: packet loss rate > 0.01 measurement period: 15 second Both results are the same in 84% of the periods of simultaneous inference Comparison We chose a path on which segments are inferred by both methods frequently. To compare simply, we summarized both results every minute. Inference Results over One Minute Periods

12 12 Conclusion Method of locating congested segments by actively measuring end-to-end packet loss rate & delay variations on multiple paths - based on a network tomographic approach & clustering techniqu e - allows us to find multiple deteriorated segments even when mult iple congestion occurs at different places on either the same path or different respective paths. Measurement on 30 paths for 10 weeks - about 90% of the clustering results were consistent with route information


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