reyna
Uploaded by
17 SLIDES
273 VUES
170LIKES

Scaling Network Emulation Using Topology Replication

DESCRIPTION

Explore ModelNet, Scalability, Replication in network emulation using pipe models, flows, and challenges of synchronization for scalability. Evaluate correctness, distributions, and future work.

1 / 17

Télécharger la présentation

Scaling Network Emulation Using Topology Replication

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related