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Network Traffic Modeling

Network Traffic Modeling

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Network Traffic Modeling

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  1. Network Traffic Modeling Arthur L. Blais University of Colorado, Colorado Springs

  2. Introduction • Nature of Internet Traffic • Trace-based vs. Analytic Models • Network Traffic Characteristics • Model Data and Distributions Arthur L. Blais - UCCS

  3. Nature of Internet Traffic • Highly variable demands. • Request inter-arrival rates. • File sizes and references. • Self-similarity. Arthur L. Blais - UCCS

  4. Trace-based Model • Uses actual data traces to generate workloads • Advantages • Easy to implement • Disadvantages • Difficult to change or vary • Difficult to find causes of system behavior Arthur L. Blais - UCCS

  5. Analytic Model • Mathematical models are used to generate workloads • Advantages • Capable of generating different workloads by varying workload characteristics. • Disadvantages • Difficult to construct • Need to identify important workload characteristics to model • Characteristics must be empirically measured Arthur L. Blais - UCCS

  6. Network Traffic Characteristics • Client Characteristics • Server Characteristics • Network Characteristics Arthur L. Blais - UCCS

  7. Client Characteristics • Request Rates • Sleep Time • Active Time • Inactive OFF Time (think time) • Active OFF Time (embedded references) • Request Sizes Arthur L. Blais - UCCS

  8. Server Characteristics • File Sizes • Cache Size • Temporal Locality • Number of Connections • CPU speed Arthur L. Blais - UCCS

  9. Network Characteristics • Bandwidth • Routing Path • Hop Count • Buffer Sizes • Packet Loss Rates Arthur L. Blais - UCCS

  10. Workload Models • Client Models • Server Models Arthur L. Blais - UCCS

  11. Client Workload Model • Sleep Time • Each Client has a time of day that it is awake • Fixed time assigned to each client so that the request rates from all the clients approximates the total request rate distribution for each hour of the day. • Inactive Off Time • Client think time, the amount of time after a request is received and the time the next request is made. • Pareto Distribution Arthur L. Blais - UCCS

  12. Client Workload Model • Active Off Time • Inter-arrival time for each embedded request • Weibull Distribution • Embedded References • The number of references included with the requested document • Pareto Distribution Arthur L. Blais - UCCS

  13. Server Workload Model • CPU speed • Number of Connections • File Size Distribution • Distribution Body ( <= 9020 bytes ) • Lognormal Distribution • Distribution Tail ( > 9020 bytes ) • Pareto Distribution Arthur L. Blais - UCCS

  14. Client Sleep Time • Approximates the percentage of the Total Request Rates for each hour of the day. • Each client has a wakeup time and a sleep time attribute. Arthur L. Blais - UCCS

  15. Client Hourly Request Rate Arthur L. Blais - UCCS

  16. Inactive Off Time • Time between requests • Uses a Pareto Distribution • alpha: a = 1.5 • Lower bound: (k) = 1.0 • To create a random variant x: • u ~ U(0,1) • x = k / (1.0-u)^1.0/a Arthur L. Blais - UCCS

  17. Inactive Off Time Arthur L. Blais - UCCS

  18. Active Off Time • Time between embedded references • Uses a Weibull Distribution • alpha: a = 1.46 (scale parameter) • beta: b = 0.382 (shape parameter) • To create a random variant x: • u ~ U(0,1) • x = a ( -ln( 1.0 – u ) ^ 1.0/b Arthur L. Blais - UCCS

  19. Active Off Time Arthur L. Blais - UCCS

  20. Embedded References • Number of references in the requested document • Uses a Pareto Distribution • alpha: a = 1.5 • Lower bound: (k) = 1.0 • To create a random variant x: • u ~ U(0,1) • x = k / (1.0-u)^1.0/a Arthur L. Blais - UCCS

  21. Embedded References Arthur L. Blais - UCCS

  22. File Size Distribution • Two Distributions • Body • Lognormal Distribution • Create a lookup table • Tail • Pareto Distribution • alpha: a = 1.5 • Lower bound: (k) = 1.0 • To create a random variant x: • u ~ U(0,1) • x = k / (1.0-u)^1.0/a Arthur L. Blais - UCCS

  23. File Size Distribution - Body Arthur L. Blais - UCCS

  24. File Size Distribution - Tail Arthur L. Blais - UCCS

  25. Self-similar Traffic • High Variability • Request Rate Inter-arrival Time • File Sizes • Negative impact on network performance • Modeling Characteristics • Heavy Tailed Distributions • Significant variability over a wide range of scales Arthur L. Blais - UCCS

  26. Self-similar Traffic Arthur L. Blais - UCCS

  27. References • Paul Barford and Mark Crovella, Generating Representative Web Workloads for Network and Server Performance Evaluation, Boston University, Technical Paper BU-CS-97-006, December 31, 1997 • Mark E. Crovella and Azar Bestavros, Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes, Boston University, Technical Paper BU-TR-95-015, 1995,1996, 1997 • Martin F. Arlitt and Carey L. Williamson, Internet Web Servers: Workload Characterization and Performance Implications, IEEE Transactions on Networking, Vol. 5, No. 5, October 1997 • Vern Paxon and Sally Floyd, Wide-Area Traffic: The Failure of Poisson Modeling, Lawrence Berkeley Laboratory and EECS Division, University of California, Berkeley, July 18,1995 Arthur L. Blais - UCCS