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Fast Algorithm for Network Tomography via Compressive Sensing

This research presents a fast and efficient algorithm for network tomography using compressive sensing techniques. The primary goal is to infer network characteristics such as edge and node delays from a minimal number of end-to-end measurements, overcoming the challenges of slow edge-by-edge monitoring and inaccessible nodes. By treating the problem as a sparse recovery issue, we highlight the use of random measurements and fixed network topology to enable faster delay estimations, ensuring robust network analysis and diagnostics in arbitrary topologies.

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Fast Algorithm for Network Tomography via Compressive Sensing

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  1. FRANTIC: Fast Referenced-based Algorithm for Network Tomography vIa Compressive sensing ShengCai The Chinese University of Hong Kong February 21, 2013 Caltech • MayankBakshi Minghua Chen SidharthJaggi

  2. Network Tomography • Difficulties: • Edge-by-edge (or node-by node) monitoring too slow • Inaccessible nodes • Goal: Infer network characteristics (edge or node delay) • with very fewend-to-end measurements • quickly • for arbitrary network topology

  3. Network Tomography • Observation: • only few edges (or nodes) “unknown” => sparse recovery problem

  4. Compressive Sensing ? Random m ? n k k ≤ m<n

  5. Network Tomography as a Compressive sensing Problem End-to-end delay Edge delay

  6. Network Tomography as a Compressive sensing Problem End-to-end delay Node delay

  7. Network Tomography as a Compressive sensing Problem • Random measurements Fixed network topology End-to-end delay Node delay

  8. Faster Higher Stronger

  9. 1. Better CS [BJCC12] “SHO(rt)-FA(st)” # of measurements Lower bound °CM’06 °GSTV’06 °TG’07 °SBB’06 °C’08 °IR’08 Lower bound °RS’60 Our work  °DJM’11 °MV’12,KP’12 Decoding complexity

  10. SHO(rt)-FA(st) Good Bad O(k) measurements, O(k) time Good Bad

  11. High-Level Overview A 3 3 4 4 4 4 ck ck n n k=2 k=2

  12. High-Level Overview How to guarantee the existence of leaf node How to find the leaf nodes and utilize the leaf nodes to do decoding 3 3 4 4 4 ck n k=2

  13. Bipartite Graph → Sensing Matrix d=3 Distinct weights A ck n

  14. Bipartite Graph → Sensing Matrix d=3 Distinct weights A “sparse & random” matrix ck n

  15. Sensing Matrix→ Measurement Design

  16. 2. Better mimicking of desired A

  17. Node delay estimation

  18. Node delay estimation

  19. Node delay estimation • Problems • General graph • Inaccessible nodes • Edge delay estimation

  20. Edge delay estimation

  21. Idea 1: Cancellation , ,

  22. Idea 2: “Loopy” measurements • Fewer measurements • Even if there exists inaccessible node (e.g. v3) • Go beyond 0/1 matrices (sho-fa) ,

  23. SHO-FA + Cancellations + Loopy measurements • Parameters • n = |V| or |E| • M = “loopiness” • k = sparsity • Results • Measurements: O(k log(n)/log(M)) • Decoding time: O(k log(n)/log(M)) • General graphs, node/edge delay estimation • Path delay: O(MDn/k) • Path delay: O(MD’n/k) (Steiner trees) • Path delay: O(MD’’n/k) (“Average” Steiner trees) • Path delay: ??? (Graph decompositions)

  24. Thank you謝謝

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