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Network Tomography. Venkat Padmanabhan Lili Qiu MSR Tab Meeting 22 Oct 2001. Overview. Goal: discover characteristics of internal links in network using passive, end-to-end measurements Metrics: loss rate , bandwidth Why is this interesting? finding trouble spots in the network

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Network tomography l.jpg

Network Tomography

Venkat Padmanabhan

Lili Qiu

MSR Tab Meeting

22 Oct 2001


Overview l.jpg
Overview

  • Goal: discover characteristics of internal links in network using passive, end-to-end measurements

  • Metrics: loss rate, bandwidth

  • Why is this interesting?

    • finding trouble spots in the network

      • e.g., AT&T-Sprint peering point could be congested

      • a Web site operator can keep tabs on his/her ISP and decide whether to sign up with new ISP(s)

    • deciding where to place server replicas

      • downstream of major trouble spots


Slide3 l.jpg

microsoft.com

Why is it

so slow?

AT&T

Sprint

C&W

UUNET

Earthlink

Darn, it’s slow!

AOL

Qwest


Topological metrics l.jpg
Topological Metrics

Topological metrics are poor predictors of packet loss rate

All links are not equal  need to identify the bad links


Prior work l.jpg

S

A

B

Prior Work

  • Active probing to infer link loss rate

    • multicast probes

    • striped unicast probes

  • Pros & cons

    • accurate since individual loss events identified

    • expensive because of extra probe traffic

S

A

B


Our approach l.jpg
Our Approach

  • Passive observation of existing traffic

    • measure loss rate rather than loss events

  • Active probing to discover network topology

    • can be done infrequently and in the background

server

l1

(1-l1)*(1-l2)*(1-l4) = (1-p1)

(1-l1)*(1-l2)*(1-l5) = (1-p2)

(1-l1)*(1-l3)*(1-l8) = (1-p5)

Under-constrained system of equations

l3

l2

l4

l5

l6

l7

l8

clients

p1

p2

p3

p4

p5


1 random sampling l.jpg
#1: Random Sampling

  • Randomly sample the solution space

  • Repeat this several times

  • Draw conclusions based on overall statistics

  • How to do random sampling?

    • determine loss rate bound for each link using best downstream client

    • iterate over all links:

      • pick loss rate at random within bounds

      • update bounds for other links

  • Problem: little tolerance for estimation error

server

l1

l3

l2

l4

l5

l6

l7

l8

p1

p2

p3

p4

p5

clients


2 linear optimization l.jpg
#2: Linear Optimization

Goals

  • Parsimonious explanation

  • Robust to estimation error

    Li = log(1/(1-li)), Pj = log(1/(1-pj))

    minimize Li + |Sj|

    L1+L2+L4 + S1 = P1

    L1+L2+L5 + S2 = P2

    L1+L3+L8 + S5 = P5

    Li >= 0

    Can be turned into a linear program

server

l1

l3

l2

l4

l5

l6

l7

l8

p1

p2

p3

p4

p5

clients


Results l.jpg
Results

  • Experimental setup

    • packet tracing machine at microsoft.com

    • client loss rates estimated from TCP traffic

    • trace analyzed: 2.12 hours, 100 million packets, 134475 clients

  • Validation

    • likely candidates for lossy links:

      • links that cross an inter-AS boundary

      • links that have a large delay


Slide10 l.jpg

Random Sampling

Linear Optimization

  • Of the 50 links identified as most lossy, 42-45 cross an inter-AS boundary and/or have delay > 100 ms

  • Example lossy links found:

    • San Francisco (AT&T)  Indonesia (Indo.net)

    • Sprint  PacBell in California

    • Moscow  Tyumen, Siberia (Sovam Teleport)


Simulation experiments l.jpg
Simulation Experiments

  • Advantage: no uncertainty about link loss rate!

  • Methodology

    • topologies used:

      • randomly-generated: 1000 nodes, max degree = 5-50

      • real topology obtained by tracing paths to microsoft.com clients

    • randomly-generated packet loss events at each link

      • loss rate 0-1% for 95% of links (non-lossy links) , 5-10% for 5% of links (lossy links)

  • Goodness metric: % links classified correctly

    • randomly-generated topologies: 90-94% accurate

      • lossy links alone: 85-95% found, but 30-90% false +ve

    • real topology: 85-90% accurate


Ongoing and future work l.jpg
Ongoing and Future Work

  • Large scale simulations with realistic topologies and traffic patterns

  • Better validation in the Internet setting

    • correlation with packet loss rate for new clients

    • active measurements in real time

  • Measurement from multiple sites(e.g., replicas)

  • Other protocols and metrics

    • non-TCP traffic (e.g., streaming media); link bandwidth

  • Refinement of techniques

    • “pseudo-passive” probing

    • selective active probing


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