Edgebased Network Modeling and Inference - PowerPoint PPT Presentation

1 / 20
About This Presentation
Title:

Edgebased Network Modeling and Inference

Description:

Alpha Beta Model. Causes of burstiness in network traffic (non-Gaussianity)? Mean. 99 ... beta. Rice University spin.rice.edu. 9. Traffic Bursts: A Case Study ... – PowerPoint PPT presentation

Number of Views:36
Avg rating:3.0/5.0
Slides: 21
Provided by: richar818
Category:

less

Transcript and Presenter's Notes

Title: Edgebased Network Modeling and Inference


1
Edge-basedNetwork Modelingand Inference
Vinay Ribeiro, Rolf Riedi, Richard Baraniuk
Rice University spin.rice.edu
2
INCITE Project
3
Available Bandwidth Estimation
  • Available bandwidth unused bandwidth on path
  • Key metric for data-intensive applications
  • Estimate ABW by e2e active probing

4
pathChirp Tool
  • Based on principle of self-induced congestion
  • Exponentially spaced chirp probe trains

5
Internet Experiments
  • 3 common hops between SLAC?Rice and Chicago?Rice
    paths
  • Estimates fall in proportion to introduced
    Poisson traffic

6
pathChirp Summary
  • Balances probing uncertainty principle
  • Efficient
  • performs comparably to state-of-the-art tools
  • (PathLoad, PacketPair, TOPP) using about 10x
    fewer packets
  • Robust to bursty traffic
  • incorporates multiscale statistical analysis
  • Open-source software available at spin.rice.edu
  • See poster Tuesday night

7
AlphaBeta Model
  • Causes of burstiness in network
    traffic(non-Gaussianity)?

beta
alpha
8
AlphaBeta Model
  • Causes of burstiness in network
    traffic(non-Gaussianity)?

beta
alpha
9
Traffic Bursts A Case Study
Typical non-spiky epoch
10
Traffic Bursts A Case Study
Typical spiky epoch
Typical non-spiky epoch
11
Beta Alpha
  • Bottlenecked elsewhere
  • Large RTT
  • Bottlenecked at this point
  • Large file small RTT






fractional Gaussian noise
stable Levy noise
12
spin.rice.edudsp.rice.edu
13
CAIDA Gigabit Testbed
  • Smartbit cross-traffic generator
  • Estimates track changes in available bandwidth
  • Performance improves with increasing packet size

14
Grid Computing
  • Harness global resources to improve performance

15
Application Predict Download Time
  • Dynamically schedule tasks based on bandwidth
    availability

16
Optimal Path Selection
  • Choose path to minimize download time from A to D

17
Active Probing for Bandwidth
  • Iperf, Pathload, TOPP,
  • Self-induced congestion principleincrease
    probing rate until queuing delay increases
  • Goal Minimally intrusive
  • Lightweight probing with as few packets as
    possible

18
Chirp Probing
  • Chirp exponential flight pattern of probes
  • Non-intrusive and Efficient wide range of
    probing bit rates, few packets

19
Comparison with Pathload
  • Rice ECE network
  • 100Mbps links
  • pathChirp can use 10x fewer bytes for comparable
    accuracy

20
Conclusions
  • pathChirp non-intrusive available bandwidth
    probing tool
  • Successful tests on the Internet and Gigabit
    testbed
  • Upto 10x improvement over state-of-the-art
    pathload on Rice ECE network
  • Whats next?
Write a Comment
User Comments (0)
About PowerShow.com