On the Stability of Network Distance Monitoring

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On the Stability of Network Distance Monitoring

• Yan Chen, Chris Karlof, Yaping Li and Randy Katz
• yanchen, ckarlof, yaping, randy_at_CS.Berkeley.EDU
• EECS Department
• UC Berkeley

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Introduction

• Lots of applications/services may benefit from
  end-to-end distance monitoring/estimation
• Mirror Selection - VPN management/provisioning
• Overlay Routing/Location - Peer-to-peer file
  system
• Cache-infrastructure Configuration
• Service Redirection/Placement
• Problem formulation
• Given N end hosts that may belong to different
  administrative domains, how to select a subset of
  them to be probes and build an overlay distance
  monitoring service without knowing the underlying
  topology?
• Solution Internet Iso-bar
• Cluster of hosts that perceive similar
  performance to Internet
• For each cluster, select a monitor for active and
  continuous probing
• The first one for monitoring site selection and
  stability evaluation with real Internet
  measurement data
• Compare with other distance estimation services
  ID Maps, GNP

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Related Work

• Existing Internet E2E distance estimation systems
  fall in two categories
• Clustering based (service-centric) IDMaps,
  Network Distance Map, Internet Iso-bar
• Coordinate based (end host-centric) Triangulated
  schemes, GNP
• Pioneering work IDMaps
• Clustering with IP address prefix (not very
  accurate)
• Based on triangulation inequality
• Number of hops only - No dynamics nor stability
  addressed
• Network Distance Map
• Clustering based on network proximity rather than
  similarity
• Fixed monitors, no monitor placement/selection
• GNP
• Each client maintains its own coordinate
• Distance estimated through certain distance
  function over the coordinates

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Framework of Internet Iso-bar

• Define correlation distance between each pair of
  hosts
• Apply generic clustering methods below
• Limit the diameter (max distance between any two
  hosts in the cluster) of a cluster, and minimize
  number of clusters
• Limit the number of clusters, then minimize the
  max diameter of all clusters
• Choose the center of each cluster as monitor
• Periodically monitors measure distance among each
  other as well as the distance to the hosts in its
  cluster
• Inter-cluster distance estimation
• dist(i,j) dist(monitori, monitorj)
• Intra-cluster distance estimation (i,j has same
  monitor m)
• dist(i,j) (dist(i, m) dist(j, m) ) / 2
• Inter-cluster estimation dominates
• Given K evenly distributed clusters, ratio of
  inter- vs. intra- estimation is K-1

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Correlation Distance

• Network distance based
• Using proximity dij measured network
  distance(pij)
• Using Euclidean distance of network distance
  vector
• Vi pi1, pi2, , pinT
• Using cosine vector similarity of network
  distance vector
• Geographical distance based
• Using proximity

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Properties Comparison
N of hosts K of monitors AP of address
prefix D of dimensions I of iterations for
optimization, proportional to of variables,
could be very large
IDMaps Internet Iso-bar GNP
Communi. cost Offline setup O(K AP) O(N2) for net_ O(N) for geo_p O(N2) for lm selection O(K2NK) for random lm
Communi. cost Online update O(K2 AP) O(K2 N) O(K2 N K)
Computation cost Offline setup O (AP K) O(N K) O(K N2 logN) O(K3 D) I(K D) O(N K D) I(D)
Computation cost Online update O(1) O(1) O(K3 D) I(K D) O(N K D) I(D)
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Evaluation Methodology

• Experiments with NLANR AMP data set
• 119 sites on US (106 after filtering out most
  off sites)
• Traceroute between every pair of hosts every
  minute
• Clustering uses daily geometric mean of
  round-trip time (RTT)
• Evaluation uses daily 1440 measurement of RTT
• Raw data
• 6/24/00
• 12/3/01

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Performance Stability Evaluation

• Compare 6 distance estimation schemes
  (denotations)
• Clustering with proximity of network distance
  (net_p)
• Clustering with Euclidean dist of network dist
  vector (net_ed)
• Clustering with vector similarity of network dist
  vector (net_vs)
• Clustering with proximity of geographical
  distance (geo_p)
• GNP - All schemes above have 15
  clusters/landmarks
• Omniscient using the original pij to predict
  future pij (omni)
• Stability analysis
• Clustering / coordinates calculation with day1
  (birth date) measurement
• Compute relative predict error (rpe) using day2
  (estimation date) measurement

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Stability CDF of relative errors for 1-month
(left) 6-month (right)
Summary of 80th (left) 90th (right) percentile
relative error
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Conclusion

• Omniscient always works the best
• RTT time overall is quite stable for the
  experimental sites and period, but need further
  verification
• It can not report timely congestion
• It requires full n n IP distance matrix,
  inapplicable to scalability tricks, e.g.
  hierarchy
• GNP has better performance and stability than
  clustering-based schemes
• Has much more computation communication cost
  when update
• Using similarity of network distance for
  clustering works much better than using proximity
• Geographical proximity based clustering works
  better than network proximity based clustering
• Requires no measurement for clustering monitor
  selection
• Provides reasonably good performance stability
• But may biased with the dataset used

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Current Work

• Congestion/Failure Correlation of Clustered Hosts
• Can Monitors report timely congestion/path
  outage? False-alarms?
• Evaluation with Keynote Web Site Perspective
  Benchmarking (Collaboration with Dr. Chris
  Overton_at_Keynote)
• Measure Web site performance from more than 100
  agents on the Internet
• Heterogeneous core network various ISPs
• Heterogeneous access network
• Dial up 56K, DSL and high-bandwidth business
  connections
• Choose 40 most popular Web servers for
  benchmarking
• Problem how to reduce the number of agents
  and/or servers, but still represent the majority
  of end-user performance for reasonably stable
  period?

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Keynote Agent Locations

• America (including Canada, Mexico) 67 agents
• 29 cities Houston, Toronto, LA, Minneapolis, DC,
  Boston, Miami, Dallas, NY, SF, Cleveland,
  Philadelphia, Milwaukee, Chicago, Cincinnati,
  Portland, Vancouver, Seattle, Phoneix, San Diego,
  Denver, Sunnyvale, McLean, Atlanta, Tampa, St.
  Louis, Mexico, Kansas City, Pleasonton
• 14 ISPs PSI, Verio, UUNET, CW, Sprint, Qwest,
  Genuity, ATT, XO, Exodus, Level3, Intermedia,
  Avantel, SBC
• Europe 25 agents
• 12 cities London, Paris, Frankfurt, Munich,
  Oslo, Copenhagen, Amsterdam, Helsinki, Milan,
  Stockholm, Madrid, Brussels
• 16 ISPs PSI, Cerbernet, Oleane, Level3, ECRC,
  Nextra, UUNET, TeleDanmark, KPNQwest, Inet, DPN,
  Xlink, Telia, Retevision, BT, Telephonica
• Asia 8 agents
• 6 cities Seoul, Singapore, Tokyo, Shanghai,
  Hongkong, Taipei
• 8 ISPs BORANet, SingTel, IIJ, ChinaTel, HKT,
  Kornet, NTTCOM, HiNet,
• Australia 3 agents
• 3 cities Sydney, Wellington, Melbourne
• 3 ISPs OzeMail, Telstra-Saturn, Optus

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Evaluation of Generic Clustering Algorithms

• Limit-number clustering and limit-diameter
  clustering gives similar results with
  Limit-number a bit better
• Net_ed and Net_vs gives similar results with
  Net_vs a bit better
• Use Limit-number clustering for the rest
  comparison

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Performance Evaluation

• Static and stability analysis in daily,
  tri-daily, weekly, bi-weekly, monthly,
  six-monthly intervals

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