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

Introduction Nature of Internet Traffic Trace-based vs. Analytic Models Network Traffic Characteristics Model Data and Distributions February 18, 2019 Arthur L. Blais - UCCS

Nature of Internet Traffic Highly variable demands. Request inter-arrival rates. File sizes and references. Self-similarity. February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

Network Traffic Characteristics Client Characteristics Server Characteristics Network Characteristics February 18, 2019 Arthur L. Blais - UCCS

Client Characteristics Request Rates Sleep Time Active Time Inactive OFF Time (think time) Active OFF Time (embedded references) Request Sizes February 18, 2019 Arthur L. Blais - UCCS

Server Characteristics File Sizes Cache Size Temporal Locality Number of Connections CPU speed February 18, 2019 Arthur L. Blais - UCCS

Network Characteristics Bandwidth Routing Path Hop Count Buffer Sizes Packet Loss Rates February 18, 2019 Arthur L. Blais - UCCS

Workload Models Client Models Server Models February 18, 2019 Arthur L. Blais - UCCS

Client Workload Model Sleep Time Inactive Off 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 February 18, 2019 Arthur L. Blais - UCCS

Client Workload Model Active Off Time Embedded References Inter-arrival time for each embedded request Weibull Distribution Embedded References The number of references included with the requested document Pareto Distribution February 18, 2019 Arthur L. Blais - UCCS

Server Workload Model CPU speed Number of Connections File Size Distribution Distribution Body ( <= 9020 bytes ) Lognormal Distribution Distribution Tail ( > 9020 bytes ) Pareto Distribution February 18, 2019 Arthur L. Blais - UCCS

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. February 18, 2019 Arthur L. Blais - UCCS

Client Hourly Request Rate February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

Inactive Off Time February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

Active Off Time February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

Embedded References February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

File Size Distribution - Body February 18, 2019 Arthur L. Blais - UCCS

File Size Distribution - Tail February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS

Self-similar Traffic February 18, 2019 Arthur L. Blais - UCCS

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 February 18, 2019 Arthur L. Blais - UCCS