Tomographybased Overlay Network Monitoring and its Applications

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Tomography-based Overlay Network Monitoring and
its Applications
Yan Chen

• Joint work with David Bindel, Brian Chavez,
  Hanhee Song, and Randy H. Katz
• UC Berkeley

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Motivation

• Applications of end-to-end distance monitoring
• Overlay routing/location
• Peer-to-peer systems
• VPN management/provisioning
• Service redirection/placement
• Cache-infrastructure configuration
• Requirements for E2E monitoring system
• Scalable efficient small amount of probing
  traffic
• Accurate capture congestion/failures
• Incrementally deployable
• Easy to use

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Existing Work

• Static estimation
• Global Network Positioning (GNP)
• Dynamic monitoring
• Loss rates RON (n2 measurement)
• Latency IDMaps, Dynamic Distance Maps, Isobar
• Latency similarity under normal conditions
  doesnt imply similar losses !
• Network tomography
• Focusing on inferring the characteristics of
  physical links rather than E2E paths
• Limited measurements -gt under-constrained system,
  unidentifiable links

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Problem Formulation

• Given n end hosts on an overlay network and O(n2)
  paths, how to select a minimal subset of paths to
  monitor so that the loss rates/latency of all
  other paths can be inferred.

• Key idea select a basis set of k paths that
  completely describe all O(n2) paths (k O(n2))
• Select and monitor k linearly independent paths
  to compute the loss rates of basis set
• Infer the loss rates of all other paths
• Applicable for any additive metrics, like latency

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Path Matrix and Path Space

• Path loss rate p, link loss rate l
• Totally s links, path vector v

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Sample Path Matrix

• x1 - x2 unknown gt cannot compute x1, x2
• Set of vectors
• form null space
• To separate identifiable vs. unidentifiable
  components x xG xN
• All E2E paths are in path space, i.e., GxN 0

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Intuition through Topology Virtualization

• Virtual links minimal path segments whose loss
  rates uniquely identified
• Can fully describe all paths
• xG similar forms as virtual links

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Rank(G)1
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Rank(G)2
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Rank(G)3
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Virtualization
Real links (solid) and overlay paths (dotted)
going through them
Virtual links
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Algorithms

• Select k rank(G) linearly independent paths to
  monitor
• Use rank revealing decomposition, e.g., QR with
  column pivoting
• Leverage sparse matrix time O(rk2) and memory
  O(k2)
• E.g., 10 minutes for n 350 (r 61075) and k
  2958
• Compute the loss rates of other paths
• Time O(k2) and memory O(k2)

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How much measurement saved ?

• k O(n2) ?
• For power-law Internet topology, M nodes, N end
  hosts
• There are O(M) links and N gt M/2 (with proof)
• If n O(N), overlay network has O(n) IP links, k
  O(n)

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When a Small Portion of End Hosts on Overlay

• Internet has moderate hierarchical structure
  TGJ02
• If a pure hierarchical structure (tree) k O(n)
• If no hierarchy at all (worst case, clique) k
  O(n2)
• Internet should fall in between

For reasonably large n, (e.g., 100), k O(nlogn)
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Practical Issues

• Topology measurement errors tolerance
• Care about path loss rates than any interior
  links
• Poor router alias resolution present show
  multiple links for one gt assign similar loss
  rates to all the links
• Unidentifiable routers gt ignore them as
  virtualization
• Topology changes
• Add/remove/change one path incurs O(k2) time
• Topology relatively stable in order of a day gt
  incremental detection

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Evaluation

• Simulation
• Topology
• BRITE Barabasi-Albert, Waxman, hierarchical 1K
  20K nodes
• Real router topology from Mercator 284K nodes
• Fraction of end hosts on the overlay 1 - 10
• Loss rate distribution (90 links are good)
• Good link 0-1 loss rate bad link 5-10 loss
  rates
• Good link 0-1 loss rate bad link 1-100 loss
  rates
• Loss model
• Bernouli independent drop of packet
• Gilbert busty drop of packet
• Path loss rate simulated via transmission of 10K
  pkts
• Metric path loss rate estimation accuracy
• Absolute/relative errors
• Lossy path inference

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Experiments on Planet Lab

• 51 hosts, each from different organizations
• 51 50 2,550 paths
• Simultaneous loss rate measurement
• 300 trials, 300 msec each
• In each trial, send a 40-byte UDP pkt to every
  other host
• Simultaneous topology measurement
• Traceroute
• Experiments 6/24 6/27
• 100 experiments in peak hours

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Tomography-based Overlay Monitoring Results

• Loss rate distribution
• Accuracy
• On average k 872 out of 2550
• Absolute error p p
• Average 0.0027 for all paths, 0.0058 for lossy
  paths
• Relative error

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Absolute and Relative Errors

• For each experiment, get its 95 percentile
  absolute and relative errors for estimation of
  2,550 paths

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Lossy Path Inference Accuracy

• 90 out of 100 runs have coverage over 85 and
  false positive less than 10
• Many caused by the 5 threshold boundary effects

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Topology Measurement Error Tolerance

• Out of 13 sets of pair-wise traceroute
• On average 248 out of 2550 paths have no or
  incomplete routing information
• No router aliases resolved
• Conclusion robust against topology measurement
  errors

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Performance Improvement with Overlay

• With single-node relay
• Loss rate improvement
• Among 10,980 lossy paths
• 5,705 paths (52.0) have loss rate reduced by
  0.05 or more
• 3,084 paths (28.1) change from lossy to
  non-lossy
• Throughput improvement
• Estimated with
• 60,320 paths (24) with non-zero loss rate,
  throughput computable
• Among them, 32,939 (54.6) paths have throughput
  improved, 13,734 (22.8) paths have throughput
  doubled or more
• Implications use overlay path to bypass
  congestion or failures

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Adaptive Overlay Streaming Media
Stanford
UC San Diego
UC Berkeley
X
HP Labs

• Implemented with Winamp client and SHOUTcast
  server
• Congestion introduced with a Packet Shaper
• Skip-free playback server buffering and
  rewinding
• Total adaptation time lt 4 seconds

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Adaptive Streaming Media Architecture
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Conclusions

• A tomography-based overlay network monitoring
  system
• Given n end hosts, characterize O(n2) paths with
  a basis set of O(nlogn) paths
• Selectively monitor O(nlogn) paths to compute the
  loss rates of the basis set, then infer the loss
  rates of all other paths
• Both simulation and real Internet experiments
  promising
• Built adaptive overlay streaming media system on
  top of monitoring services
• Bypass congestion/failures for smooth playback
  within seconds

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