Self similarity in world wide web traffic evidence and possible causes
Download
1 / 16

Self Similarity in World Wide Web: Traffic Evidence and Possible Causes - PowerPoint PPT Presentation


  • 98 Views
  • Uploaded on

Self Similarity in World Wide Web: Traffic Evidence and Possible Causes. Mark E. Crovella and Azer Bestavros Computer Science Dept, Boston University. Presented by Kalyan Boggavarapu CSC 497 Lehigh University. Self-Similarity.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Self Similarity in World Wide Web: Traffic Evidence and Possible Causes' - scarlet-christensen


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Self similarity in world wide web traffic evidence and possible causes

Self Similarity in World Wide Web: Traffic Evidence and Possible Causes

Mark E. Crovella and Azer Bestavros

Computer Science Dept,

Boston University

Presented by

Kalyan Boggavarapu

CSC 497

Lehigh University


Self similarity
Self-Similarity

  • Def: is an object whose appearance is unchanged regardless of the scale it is used.

  • Heavy tailed:

    • a function exhibiting the power laws.

    • E.g.: The geographical distribution of the people in the world.

  • World Wide Web traffic can show Self-Similarity

Kalyan Boggavarapu CSC 497 Lehigh University


Data set
Data Set

  • Traces from NCSA Mosaic

  • Jan, Feb 1995

  • Logs: URL, session, User and workstation ID

  • Experiment Environment:

    • 37 SparkStation-2 workstations,

Kalyan Boggavarapu CSC 497 Lehigh University




Parameters
Parameters

  • Degree of self Similarity - H

    • Hurst parameter H ,range of (1/2 , 1)

    • H->1 is the max self-similarity

    • In this paper we would see

Kalyan Boggavarapu CSC 497 Lehigh University


Analysis in two stages
Analysis in two stages

  • Stage 1:

    • what is the appropriate value of H.

  • Stage 2:

    • Which parameter accurately measures this parameter H.

Kalyan Boggavarapu CSC 497 Lehigh University


Stage 1 estimate the value of h

Stage 1: Estimate the value of H


Self similarity for different time intervals
Self Similarity for different time intervals

  • Step 1:

    • Estimate for short intervals ( 1 sec and above )

      • using: web traffic data for a single hr

        • Plot:

          • Variance Time plot,

          • Rescaled range plot

          • Periodogram plot

  • Step 2:

    • Estimate for scaling to large intervals

      • Whittle Estimator

Kalyan Boggavarapu CSC 497 Lehigh University


Self similarity characteristics graphs 1
Self Similarity characteristics graphs 1

Slope => H

This line is => H

Slope is => H

Kalyan Boggavarapu CSC 497 Lehigh University


Whilttle estimator
Whilttle Estimator

  • Estimates: the confidence range of H

  • Based: a time series

    • FGN – Fractional Gaussian Noise Model

  • Now check: if timeseries aggregation or

  • Estimated H is consistent or not ?

  • Infer: www traffic at stub networks is self similar when traffic is high in demand.

  • Kalyan Boggavarapu CSC 497 Lehigh University


    Expected feature: aggregation => H

    Aggregation over a long range shows stability of the hypothesis

    Whittle estimator confirms our earlier calculations of H

    H

    Fully busy

    Variance of

    95% Confidence Interval of H

    Least busy

    H decreasing as it becomes less busy

    Kalyan Boggavarapu CSC 497 Lehigh University


    Stage 2 which parameter is useful to estimate the value of h

    Stage 2:Which parameter is useful to estimate the value of H


    Which parameter is responsible for self similarity
    Which parameter is responsible for self similarity?

    File requests => file transfers => unique files distribution

    Alpha = 1.2

    H (.7-.8)

    Kalyan Boggavarapu CSC 497 Lehigh University


    Its available files
    Its Available files

    Available files => Heavy tailed behavior of file transfer

    Conclusion:

    Distribution of available files

    =>

    ( Web traffic self similarity =

    Heavy tailed distribution of file transfers)

    Kalyan Boggavarapu CSC 497 Lehigh University


    Sources
    Sources:

    • “Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes” (1996) Mark Crovella, Azer Bestavros Proceedings of SIGMETRICS'96: The ACM International Conference on Measurement and Modeling of Computer Systems.

    Kalyan Boggavarapu CSC 497 Lehigh University


    ad