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On the Stability of Network Distance Monitoring

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Yan Chen, Chris Karlof, Yaping Li and Randy Katz {yanchen, ckarlof, yaping, randy}_at_CS.Berkeley.EDU EECS Department UC Berkeley – PowerPoint PPT presentation

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Title: On the Stability of Network Distance Monitoring


1
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

2
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

3
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

4
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

5
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

6
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)
7
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

8
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

9
Stability CDF of relative errors for 1-month
(left) 6-month (right)
Summary of 80th (left) 90th (right) percentile
relative error
10
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

11
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?

12
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

13
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

14
Performance Evaluation
  • Static and stability analysis in daily,
    tri-daily, weekly, bi-weekly, monthly,
    six-monthly intervals

15
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