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

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 FREECONSTRAINED Backoff / Set timer, bw=bw * b 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

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?