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Scaling Network Emulation Using Topology Replication

Scaling Network Emulation Using Topology Replication. Second Year Project Advisor : Amin Vahdat Committee: Jeff Chase, Jun Yang. Outline. ModelNet Scalability Replication Motivation Theory Evalution Conclusions and Future Work. Model network links as pipes Pipe is a queue

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Scaling Network Emulation Using Topology Replication

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  1. Scaling Network Emulation Using Topology Replication Second Year Project Advisor : Amin Vahdat Committee: Jeff Chase, Jun Yang

  2. Outline • ModelNet • Scalability • Replication • Motivation • Theory • Evalution • Conclusions and Future Work

  3. Model network links as pipes Pipe is a queue Bandwidth, loss rate, latency,queuing discipline etc. Multiple flows share the same pipe(capture congestion) ModelNet

  4. Replication • Emulation capacity limited by the core • For a fixed topology measured in terms of packets per second processed • Need more processors to scale • Key observation: Not one flow is responsible for the breakdown but a lot of them collectively • Intuition: Somehow let different processors handle different flows. • The challenge: How to synchronise so that the processor in unison emulate the behaviour expected

  5. System Design

  6. State diagram for a pipe Backoff / Set timer, bw=bw * b FREE CONSTRAINED Timeout / Reset bw = bw_max State diagram for a pipe

  7. Evaluation • Long Lived Flows • Short Web like flows What to measure ? • Correctness/Accuracy • Overhead

  8. Web like flows Client Client

  9. How to measure correctness • Measure file access times for 10 files each from 10 clients • Network measurement=> large variance in results=> use Statistics • Perform the test a large number of times(>30)=> 100 distributions • Repeat with replication on=>100 distributions • Compare pairwise=> 100 comparisons • What does comparison mean?

  10. Comparing Distributions • Kolmogorov-Smirnov Test (KStest) • Null Hypothesis=> CDFa=CDFb • Set significance level =0.05 • Unable to reject null hypothesis-> with large confidence CDFa=CDFb • Results => unable to detect for 90 pairs(90%)

  11. Comparing Distributions • For at least 90% the cdfs seem to match.(Null hypothesis not rejected) • For remaining 10 % • Compare 90-ile of bandwidths seen • Calculating percentage deviation of cfm compared to logical pipe • [Plot/quote error in throughput for these cases] • Other ways?

  12. Q-Q plot • A straight line here means that both samples are drawn from the same underlying distribution

  13. Percentage error for 90-iles • The cdf is good if the 90-ile has a low percentage error

  14. Scalability • Havent gone into this at all. Scaling with more number of cores? • [possibly graph showing more pps when the partitioning case has a lot of cross-traffic]

  15. Overhead • Have a loose bound on the communication overhead which does not seem bad but more can be future work

  16. Conclusions and Future Work • Showed replication as an effective technique to scale. • Parameters are application specific but more work required to quanitfy their roles. • Partitioning along with replication should be considered • A more realistic application evaluated

  17. Thanks! Questions?

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