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Model Assisted Path Characteristic Inference

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Title: Model Assisted Path Characteristic Inference


1
Model Assisted Path Characteristic Inference
  • Wei Wei
  • Dept. of Computer Science
  • UMass, Amherst
  • weiwei_at_cs.umass.edu

2
Virtual Queuing Delay Distribution of Loss Packets
  • Virtual Queuing Delay (VQD) - Would-be queuing
    delay if loss packet is not lost
  • Virtual Queuing Delay Distribution (VQDD) of loss
    packets plays key role in inferring some path
    characteristics
  • Is there a dominant congested link along the
    path?
  • What is the smallest full queuing delay for
    routers along the path?

3
Existing Method to Infer VQDD
  • Loss pair approach
  • J. Liu, M. Crovella, "Using Loss Pairs to
    Discover Network Properties," in Proceedings of
    the ACM SIGCOMM Internet Measurement Workshop
    2001, CA, November 2001
  • Send packet pairs
  • If one of the packets is lost (loss pair), use
    the delay of the packet that is not lost to get
    VQDD
  • Otherwise, throw the trace away

4
Trace Utilization of Loss Pair Approach
  • p loss rate, q 1-p, Bernoulli loss
  • n of loss pairs
  • Average of packet to generate n loss pairs
  • n/(pq)
  • Trace Utilization
  • U n/(n/(pq) pq lt 1/4
  • the equality holds when p 0.5
  • Assume we need 1,000 loss pairs to get good VQDD
    estimate, send 100 packets per second
  • p 0.05, U 0.0475, 21053 packets, 211sec
  • p 0.01, U 0.0099, 101010 packets, 1010sec
  • p 0.001, U 0.000999, 1 million packets,
    2.8hours

5
Trace Utilization of Model Assisted Approach
  • One way periodic probing
  • Use all the delay and loss trace to construct a
    model and estimate VQDD
  • Trace Utilization 100
  • Assume we need 100 loss packets to obtain good
    VQDD estimate, send 100 packets per second
  • p 0.05, 2,000 packets, 20 sec
  • p 0.01, 10,000 packets, 100 sec
  • p 0.001, 100,000 packets, 1000sec

6
Challenge
  • Construct a Model which can estimate VQDD
    accurately
  • Interpolating method?
  • NO. Naïve method.
  • Modified HMM which can handle missing data?
  • NO. Delay is not included in the state space.
    VQDD estimation is not stable with respect to
    of parameters and initial model selection.
  • HMM which can handle missing data and with both
    hidden states and observations in state space?
  • YES! Good match with virtual probes. VQDD is
    stable with respect to of parameters and
    initial model selection.

7
Some Results
Two lossy links
One Lossy Link
8
On the Existence of Dominant Congested Link
  • ns simulations
  • Simulation setup
  • Topology 3 links, link bandwidths lt 10M
  • Loss rate around 5
  • Loads of background traffic, cross traffic (http,
    TCP, UDP,)
  • 100 percent accurate when trace is longer than
    1 minute
  • Internet experiments
  • UMASS to UFRJ One dominant congested link on
    Brazil size. Which link?
  • UMASS to USC No dominant congested link

9
Thanks !
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