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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

Network Tomography

Venkat Padmanabhan

Lili Qiu

MSR Tab Meeting

22 Oct 2001

overview
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

microsoft.com

Why is it

so slow?

AT&T

Sprint

C&W

UUNET

Earthlink

Darn, it’s slow!

AOL

Qwest

topological metrics
Topological Metrics

Topological metrics are poor predictors of packet loss rate

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

prior work

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
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
#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
#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
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

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
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
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