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Wang Yang Southeast University August 2008

FAME : Factor Analysis Based Metrics Exploring Algorithm. Wang Yang Southeast University August 2008. Outline. Introduction Basic of FA FAME algorithm Experiments Conclusion and Future work. Introduction. Basics of Metrics. Basic of network behavior research

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Wang Yang Southeast University August 2008

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  1. FAME : Factor Analysis Based Metrics Exploring Algorithm Wang Yang Southeast University August 2008

  2. Outline • Introduction • Basic of FA • FAME algorithm • Experiments • Conclusion and Future work

  3. Introduction • Basics of Metrics • Basic of network behavior research • we need different metrics to describe different network research objects’ behavior. • Example • the Object of network behavior research • different levels: link, packets, flows, sessions

  4. Introduction • Basics of Metrics • Atomic metrics • Describes the object’s direct property that cannot be further decomposition • Derivative metrics • Derived from the atomic metric through limited elementary operations and can reflect the characteristics of the object.

  5. Introduction • Atomic metrics exploring method • Rules: measurability, repeatability of measuring process • Research instinct • Enumerate every possibility • IETF IPPM WG: connectivity; one-way delay; one-way packet loss rate

  6. Introduction • Derivative metrics exploring method • Enumerate different operations on atomic metrics • Andrew Moore : mean, variance, FFT

  7. Introduction • Shortcoming • Atomic metrics: • Reflect what, no why and how • Derivative metrics • There is no systematic method • Lots of useless metrics • We need a systematic method

  8. Basics of FA • What is Factor Analysis • originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data. • a statistical method used to explain variability among observed variables in terms of fewer unobserved variables called factors. • The information gained about the interdependencies can be used later to reduce the set of variables in a dataset.

  9. Basics of FA • FA Example • Spearman • a wide variety of mental tests could be explained by a single underlying intelligence factor (a notion now rejected).

  10. Basics of FA • Schema for common factor theory

  11. Basics of FA • Mathematical model • X is a matrix of observable variables • F is a m × l matrix of unobservable random variables • aijis factor loading that explain the relationship between the source metrics and the factor metrics

  12. FAME Algorithm • Algorithm Select original metrics’ matrix X ; Get X’s observing experiment data x through measuring process; Test x to determine whether x isfit for factor analysis process. If the answer is yes, then go to the 4th step, else go to the 1st step to reselect metrics; Get factor loading matrix A through factor analysis process; Give each factor semantic meaning through A.

  13. Experiment • Experiment Setup • Environment • Netflow Data aggravated by host • Captured at CERNET X Province border Router (Cisco 7609) • SPSS 15 • Two type of data • same time range all-IP traffic data • same IP different time traffic data

  14. Experiment • Original metrics

  15. Experiment • Same time range all-IP traffic data

  16. Experiment • Same time range all-IP traffic data • Four factors • active factor • the level of the user interaction activity with the outside world • throughput factor • reflects the host throughput from the view of the number of packets, the number of bytes and the number of flows • load factor • the host tendency of providing or acquiring traffics • role factor • the host user is client/Server/P2P point

  17. Experiment • same IP different time traffic data

  18. Experiment • same IP different time traffic data • two factors • active factor • the level of the user interaction activity with the outside world • role factor • the host user is client/Server/P2P point

  19. Conclusion and Future work • Conclusion • Factor Analysis is a systematic method to exploring derivative metrics • Factor metrics can help explain and reduce the source atomic and derivative metrics. • Future work • how to select source variables for factor analysis • how to computer the value of the factor metrics

  20. Reference • V. Paxson, G. Almes, J. Mahdavi. Framework for IP Performance Metrics, RFC 2330, May 1998 • W. Moore and D. Zuev, Discriminators for use in flow-based classification, Technical report, Intel Research, Cambridge, 2005. • Mingzhong Zhou, Study of Large-scale Network IP Flows behavior Characteristics and Measurement Algorithms. Phd. Thesis, Southeast University, August 2006.

  21. Questions? Thank You

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