Server-based Characterization and Inference of Internet Performance

1
Server-based Characterization and Inference of
Internet Performance

• Venkat Padmanabhan
• Lili Qiu
• Helen Wang
• Microsoft Research
• UCLA/IPAM Workshop
• March 2002

2
Outline

• Overview
• Server-based characterization of performance
• Server-based inference of performance
• Passive Network Tomography
• Summary and future work

3
Overview

• Goals
• characterize end-to-end performance
• infer characteristics of interior links
• Approach server-based monitoring
• passive monitoring ? relatively inexpensive
• enables large-scale measurements
• diversity of network paths

4
Web server
ACKs
ACKs
DATA
clients
5
Research Questions

• Server-based characterization of end-to-end
  performance
• correlation with topological metrics
• spatial locality
• temporal stability
• Server-based inference of internal link
  characteristics
• identification of lossy links

6
Related Work

• Server-based passive measurement
• 1996 Olympics Web server study (Berkeley, 1997
  1998)
• characterization of TCP properties (Allman 2000)
• Active measurement
• NPD (Paxson 1997)
• stationarity of Internet path properties (Zhang
  et al. 2001)

7
Experiment Setting

• Packet sniffer at microsoft.com
• 550 MHz Pentium III
• sits on spanning port of Cisco Catalyst 6509
• packet drop rate lt 0.3
• traces up to 2 hours long, 20-125 million
  packets, 50-950K clients
• Traceroute source
• sits on a separate Microsoft network, but all
  external hops are shared
• infrequent and in the background

8
Topological Metrics and Loss Rate
Topological distance is a poor predictor of
packet loss rate. All links are not equal ? need
to identify the lossy links
9
Spatial Locality

• Do clients in the same cluster see similar loss
  rates?
• Loss rate is quantized into buckets
• 0-0.5, 0.5-2, 2-5, 5-10, 10-20, 20
• suggested by Zhang et al. (IMW 2002)
• Focus on lossy clusters
• average loss rate gt 5

Spatial locality ? there may be shared cause for
packet loss
10
Temporal Stability

• Loss rate again quantized into buckets
• Metric of interest stability period (i.e., time
  until transition into new bucket)
• Median stability period 10 minutes
• Consistent with previous findings based on active
  measurements

11
Putting it all together

• All links are not equal ? need to identify the
  lossy links
• Spatial locality of packet loss rate ? lossy
  links may well be shared
• Temporal stability ? worthwhile to try and
  identify the lossy links

12
Passive Network Tomography

• Goal determine characteristics of internal
  network links using end-to-end, passive
  measurements
• We focus on the link loss rate metric
• primary goal identifying lossy links
• Why is this interesting?
• locating trouble spots in the network
• keeping tabs on your ISP
• server placement and server selection

13
Web server
Why is it so slow?
ATT
Sprint
CW
Earthlink
UUNET
Darn, its slow!
AOL
Qwest
14
Related Work

• MINC (Caceres et al. 1999)
• multicast-based active probing
• Striped unicast (Duffield et al. 2001)
• unicast-based active probing
• Passive measurement (Coates et al. 2002)
• look for back-to-back packets
• Shared bottleneck detection
• Padmanabhan 1999, Rubenstein et al. 2000, Katabi
  et al. 2001

15
Active Network Tomography
S
A
B
Striped unicast probes
Multicast probes
16
Problem Formulation
server
Collapse linear chains into virtual
links (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
l1
l3
l2
l8
l7
l6
l4
l5
p1
p2
p3
p4
p5
clients
17
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
l8
l7
l6
l4
l5
p1
p2
p3
p4
p5
clients
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2 Linear Optimization

• Goals
• Parsimonious explanation
• Robust to estimation error
• Li log(1/(1-li)), Pj log(1/(1-pj))
• minimize ?Li ?Sj
• L1L2L4 S1 P1
• L1L2L5 S2 P2
• L1L3L8 S5 P5
• Li gt 0
• Can be turned into a linear program

server
l1
l3
l2
l8
l7
l6
l4
l5
p1
p2
p3
p4
p5
clients
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3 Bayesian Inference

• Basics
• D observed data
• sj packets successfully sent to client j
• fj packets that client j fails to receive
• T unknown model parameters
• li packet loss rate of link i
• Goal determine the posterior P(TD)
• inference is based on loss events, not loss rates
• Bayes theorem
• P(TD) P(DT)P(T)/?P(DT)P(T)dT
• hard to compute since T is multidimensional

server
l1
l3
l2
l8
l7
l6
l4
l5
(s1,f1)
(s2,f2)
(s3,f3)
(s4,f4)
(s5,f5)
clients
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Gibbs Sampling

• Markov Chain Monte Carlo (MCMC)
• construct a Markov chain whose stationary
  distribution is P(TD)
• Gibbs Sampling defines the transition kernel
• start with an arbitrary initial assignment of li
• consider each link i in turn
• compute P(liD) assuming lj is fixed for j?i
• draw sample from P(liD) and update li
• after burn-in period, we obtain samples from the
  posterior P(TD)

21
Gibbs Sampling Algorithm

• 1) Initialize link loss rates arbitrarily
• 2) For j 1 burn-in for each link i
  compute P(liD, li) where li is
  loss rate of link i, and li ?j?i lj
• 3) For j 1 realSamples for each link
  i compute P(liD, li)
• Use all the samples obtained at step 3 to
  approximate P(?D)

22
Experimental Evaluation

• Simulation experiments
• Internet traffic traces

23
Simulation Experiments

• Advantage no uncertainty about link loss rate
• Methodology
• Topologies used
• randomly-generated 20 - 3000 nodes, max degree
  5-50
• real topology obtained by tracing paths to
  microsoft.com clients
• randomly-generated packet loss events at each
  link
• a fraction f of the links are good, and the rest
  are bad
• LM1 good links 0 1, bad links 5 10
• LM2 good links 0 1, bad links 1 100
• Goodness metrics
• Coverage correctly inferred lossy links
• False positives incorrectly inferred lossy
  links

24
Simulation Results
25
Simulation Results
26
Simulation Results
High confidence in top few inferences
27
Trade-off
Techniques Coverage False Positive Computation
Random sampling High High Low
LP Medium Low Medium
Gibbs sampling High Low High
28
Internet Traffic Traces

• Challenge validation
• Divide client traces into two tomography set and
  validation set
• Tomography data set gt loss inference
• Validation set gt check if clients downstream of
  the inferred lossy links experience high loss
• Results
• false positive rate is between 5 30
• likely candidates for lossy links
• links crossing an inter-AS boundary
• links having a large delay (e.g. transcontinental
  links)
• links that terminate at clients
• example lossy links
• San Francisco (ATT) ? Indonesia (Indo.net)
• Sprint ? PacBell in California
• Moscow ? Tyumen, Siberia (Sovam Teleport)

29
Summary

• Poor correlation between topological metrics
  performance
• Significant spatial locality and temporal
  stability
• Passive network tomography is feasible
• Tradeoff between computational cost and accuracy
• Future directions
• real-time inference
• selective active probing
• Acknowledgements
• MSR Dimitris Achlioptas, Christian Borgs,
  Jennifer Chayes, David Heckerman, Chris Meek,
  David Wilson
• Infrastructure Rob Emanuel, Scott Hogan
• http//www.research.microsoft.com/padmanab