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Simple Network Performance Tomography

Simple Network Performance Tomography. CS743 Spring 2004 Andreas Terzis. Background. It used to be the case that we need very little about network conditions Delay Loss Rate Availability and Stability of routes

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Simple Network Performance Tomography

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  1. Simple Network Performance Tomography CS743 Spring 2004 Andreas Terzis CS743 Spring 2004

  2. Background • It used to be the case that we need very little about network conditions • Delay • Loss Rate • Availability and Stability of routes • Vern Paxson did one of the first systematical studies in this area [Pax96], [Pax97] • Used traceroute and ping to measure delay and packet loss to a large number of destinations CS743 Spring 2004

  3. Limitations • Many routers today do not respond to ICMP messages • Can be used for attacks • Alternative • Have measurement nodes at both ends • Use existing information (e.g. RTP loss data or TCP retx) to infer characteristics CS743 Spring 2004

  4. Tomography • Try to infer characteristics of the network internals from end-to-end measurements • Setup • Mesh of intersecting network paths • Measure end-to-end performance along each path • Correlate measurements to infer performance on common paths • Limitation • Might be difficult to deploy a large set of measurement points CS743 Spring 2004

  5. Techniques • Multicast based tomography • Send probes down mcast tree • Packet performance identical on common path • One-to-one map from link-performance to end-to-end performance • Infer link loss rate from measured end-to-end loss rate • Limitation • Mcast not widely deployed CS743 Spring 2004

  6. Techniques • Use TCP retx to identify link loss rate Problem: multiple per-link loss rates can give the same E2E results CS743 Spring 2004

  7. Insight • Assume that links are either good or bad • A bad path cannot arise from “partially bad” links • Bad links are rate • Then if two paths are bad, the bad link is in the intersection of the two paths CS743 Spring 2004

  8. Paper Contributions • The worst performing links on a tree can be identified • Real networks (almost) follow the previous model • Present practical algorithm for identifying the worst performing links • With proven performance CS743 Spring 2004

  9. Model • Tree from source to list of destinations • Each link has characteristic f_i • Path characteristic is product of f_i • Examples of seperable characteristics • High-Low Loss • Delay spikes CS743 Spring 2004

  10. Smallest Consistent Failure Set • Idea: • Take the links closest to the root that are consistent with the observed pattern of bad paths • If a link is not bad then links on all subtrees are bad CS743 Spring 2004

  11. Probability of false positives CS743 Spring 2004

  12. Coverage Probability CS743 Spring 2004

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