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Towards Unbiased End-to-End Network Diagnosis

Towards Unbiased End-to-End Network Diagnosis. Yao Zhao 1 , Yan Chen 1 , David Bindel 2. Lab for Internet & Security Tech, Northwestern Univ Courant Institute of Mathematical Science , New York University. Outline. Background and Motivation MILS in Undirected Graphs MILS in Directed Graphs

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Towards Unbiased End-to-End Network Diagnosis

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  1. Towards Unbiased End-to-End Network Diagnosis Yao Zhao1, Yan Chen1, David Bindel2 • Lab for Internet & Security Tech, Northwestern Univ • Courant Institute of Mathematical Science , New York University

  2. Outline • Background and Motivation • MILS in Undirected Graphs • MILS in Directed Graphs • Evaluation • Conclusions

  3. End-to-End Network Diagnosis 93 hours?

  4. Path loss rate pi, link loss ratelj: A 1 3 D p1 p2 C 2 B Linear Algebraic Model Usually an underconstrained system G

  5. [ 1 0 0 ] ? Unidentifiable Links • Vectors That Are Linear Combinations of Row Vectors of G Are Identifiable • The property of a link (or link sequence) can be computed from the linear system if and only if the corresponding vector is identifiable • Otherwise, Unidentifiable A 1 3 D p1 p2 [ 0 0 1 ] C 2 B

  6. 0.1 0 0.1 Motivation • Biased statistic assumptions were introduced to infer unidentifiable virtual links, but can be inaccurate. Loss rate? Virtual Link Loss = 0 if unicast tomography & RED Loss rate = 0.1 if linear optimization

  7. Least-biased End-to-end Network Diagnosis (LEND) • Basic Assumptions • End-to-end measurement can infer the end-to-end properties accurately • Link level properties are independent • Problem Formulation • Given end-to-end measurements, what is the finest granularity of link properties can we achieve under basic assumptions? Better accuracy Basic assumptions More and stronger statistic assumptions Diagnosis granularity? Virtual link

  8. Least-biased End-to-end Network Diagnosis (LEND) • Contributions • Define the minimal identifiable unit under basic assumptions (MILS) • Prove that only E2E paths are MILS with a directed graph topology (e.g., the Internet) • Propose good path algorithm (incorporating measurement path properties) for finer MILS Better accuracy Basic assumptions More and stronger statistic assumptions Diagnosis granularity? Virtual link

  9. Outline • Background and Motivation • MILS in Undirected Graphs • MILS in Directed Graphs • Evaluation • Conclusions

  10. Minimal Identifiable Link Sequence • Definition of MILS • The smallest path segments with loss rates that can be uniquely identified through end-to-end path measurements • Related to the sparse basis problem • NP-hard Problem • Properties of MILS • The MILS is a consecutive sequence of links • A MILS cannot be split into MILSes (minimal) • MILSes may be linearly dependent, or some MILSes may contain other MILSes

  11. Examples of MILSes in Undirected Graph Real links (solid) and all of the overlay paths (dotted) traversing them MILSes b 3’ 1 4 e a 4’ d 1’ 3 3’+2’-1’-4’ → link 3 2 5 2’ c

  12. Identify MILSes in Undirected Graphs • Preparation • Active or passive end-to-end path measurement • Optimization • Measure O(nlogn) paths and infer the n(n-1) end-to-end paths [SIGCOMM04]

  13. Identify MILSes in Undirected Graphs • Preparation • Identify MILSes • Enumerate each link sequence to see if it is identifiable • Computational complexity: O(r×k×l2) • r: the number of paths (O(n2)) • k: the rank of G (O(nlogn)) • l: the length of the paths • Only takes 4.2 seconds for the network with 135 Planetlab hosts and 18,090 Internet paths

  14. Outline • Background and Motivation • MILS in Undirected Graphs • MILS in Directed Graphs • Evaluation • Conclusions

  15. Sum=1 Sum=1 Sum=1 Sum=1 Sum=1 Sum=0 What about Directed Graphs? • Intuition • Directed graphs is similar to undirected graph, although more complicate [1 0 0 0 0 0] ? Theorem:In a directed graph, no end-to-end path contains an identifiable subpath if only considering topology information

  16. Good Path Algorithm • Consider Only Topology • Works for undirected graph • Incorporate Measurement Path Property • Most paths have no loss • PlanetLab experiments show 50% of paths in the Internet have no loss • All the links in a path of no loss are good links (Good Path Algorithm)

  17. Good Path Algorithm • Symmetric property is broken when using good path algorithm.

  18. Other Features of LEND • Dynamic Update for Topology and Link Property Changes • End hosts join or leave, routing changes or path property changes • Incremental update algorithms very efficient • Combine with Statistical Diagnosis • Inference with MILSes is equivalent to inference with the whole end-to-end paths • Reduce computational complexity because MILSes are shorter than paths • Example: applying statistical tomography methods in [Infocom03] on MILSes is 5x faster than on paths

  19. Outline • Motivation • MILS in Undirected Graphs • MILS in Directed Graphs • Evaluation • Conclusions

  20. Evaluation Metrics • Diagnosis Granularity • Average length of all the lossy MILSes in lossy path • Accuracy • Simulations • Absolute error and relative error • Internet experiments • Cross validation • IP spoof based consistency check • Speed • Running time for finding all MILSes and loss rate inference

  21. Methodology • Planetlab Testbed • 135 end hosts, each from different institute • 18,090 end-to-end paths • Topology Measured by Traceroute • Avg path length is 15.2 • Path Loss Rate by Active UDP Probing with Small Overhead

  22. Diagnosis Granularity

  23. Distribution of Length of MILSes • Most MILSes are pretty short • Some MILSes are longer than 10 hops • Some paths do not overlap with any other paths Most MILSes are short A few MILSes are very long

  24. Other Results • MILS to AS Mapping • 33.6% lossy MILSes comprise only one physical link • 81.8% of them connect two ASes • Accuracy • Cross validation (99.0%) • IP spoof based consistency check (93.5%) • Speed • 4.2 seconds for MILS computations • 109.3 seconds for setup of scalable active monitoring [SIGCOMM04]

  25. Conclusion • Link-level property inference in directed graphs is completely different from that in undirected graphs • With the least biased assumptions, LEND uses good path algorithm to infer link level loss rates, achieving • Good inference accuracy • Acceptable diagnosis granularity in practice • Online monitoring and diagnosis • Continuous monitoring and diagnosis services on PlanetLab under construction

  26. Thank You! For more info: http://list.cs.northwestern.edu/lend/ Questions?

  27. R B A Motivation • End-to-End Network Diagnosis • Under-constrained Linear System • Unidentifiable Links exist To simplify presentation, assume undirected graph model

  28. Linear Algebraic Model (2) = …

  29. Identifiable and Unidentifiable • Vectors That Are Linear Combinations of Row Vectors of G Are Identifiable • Otherwise, Unidentifiable x3 Row(path) space (identifiable) A (0,0,1) 1 3 (1,1,1) D p1 x1 p2 C 2 B (1,1,0) x2

  30. 1’ a 1 2 Rank(G)=1 1 a 1’ 2’ 2 3 c b 3’ Rank(G)=3 b 3’ 1 4 e a 4’ d 1’ 3 2 5 2’ c Rank(G)=4 Real links (solid) and all of the overlay paths (dotted) traversing them MILSes Examples of MILSes in Undirected Graph 3’+2’-1’-4’ → link 3

  31. x3 x1 x2 Identify MILSes in Undirected Graphs • Preparation • Identify MILSes • Compute Q as the orthonormal basis of R(GT) (saved by preparation step) • For a vector v in R(GT) , ||v|| = ||QTv|| v2 v1

  32. Flowchart of LEND System • Step 1 • Monitors O(n·logn) paths that can fully describe all the O(n2) paths(SIGCOMM04) • Or passive monitoring • Step 2 • Apply good path algorithm before identifying MILSes as in undirected graph Iteratively check all possible MILSes Measure topology to get G Good path algorithm on G Active or passive monitoring Compute loss rates of MILSes Stage 2: online update the measurements and diagnosis Stage 1: set up scalablemonitoring system for diagnosis

  33. Evaluation with Simulation • Metrics • Diagnosis granularity • Average length of all the lossy MILSes in lossy path (in the unit of link or virtual link) • Accuracy • Absolute error |p – p’ |: • Relative error

  34. Simulation Methodology • Topology type • Three types of BRITE router-level topologies • Mecator topology • Topology size • 1000 ~ 20000 or 284k nodes • Number of end hosts on the overlay network • 50 ~ 300 • Link loss rate distribution • LLRD1 and LLRD2 models • Loss model • Bernoulli and Gilbert

  35. Sample of Simulation Results • Mercator (284k nodes) with Gilbert loss model and LLRD1 loss distribution

  36. Related Works • Pure End-to-End Approaches • Internet Tomography • Multicast or unicast with loss correlation • Uncorrelated end-to-end schemes • Router Response Based Approach • Tulip and Cing

  37. MILS to AS Mapping • IP-to-AS mapping constructed from BGP routing tables • Consider the short MILSes with length 1 or 2 • Consist of about 44% of all lossy MILSes. • Most lossy links are connecting two dierent ASes

  38. Accuracy Validation • Cross Validation (99.0% consistent) • IP Spoof based Consistency Checking. • UDP: Src: A, Dst: B, TTL=255 • UDP: Src: C, Dst: B, TTL=2 • ICMP: Src: R3, Dst: C, TTL=255 • UDP: Src: A, Dst: C, TTL=255 C R1 A R3 R2 B IP Spoof based Consistency: 93.5%

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