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Network-Level Parameters and Metrics

$\textstyle \parbox{0.2in}{\mbox{}}$$\textstyle \parbox{5.25in}{\begin{singlespace}\textit{If you are distressed by ...
...of it; and this you have the power to revoke at
any moment.}\end{singlespace}}$$\textstyle \parbox{0.2in}{\mbox{}}$
$\textstyle \parbox{5.25in}{\begin{flushright}--- \textsc{{Marcus Aurelius (121--180)}}\end{flushright}}$

$\textstyle \parbox{0.2in}{\mbox{}}$$\textstyle \parbox{5.25in}{\begin{singlespace}\textit{Reality continues to ruin my life.}\end{singlespace}}$$\textstyle \parbox{0.2in}{\mbox{}}$
$\textstyle \parbox{5.25in}{\begin{flushright}--- \textsc{{Bill Watterson (1958--)}}, Calvin and Hobbes\end{flushright}}$

The workload of TCP connections represents the demands of applications for sending and receiving data in a reliable, ordered, and congestion-responsive manner. How well TCP can satisfy these demands depends on the conditions of the network path between the two endpoints of each TCP connection, and the way TCP reacts to these conditions. An obvious example of a network condition that affects TCP is congestion that leads to segment loss. When a data segment is lost, TCP must retransmit it, and this implies some reduction in performance (e.g., throughput) as the same data segment (rather than a new one) has to be sent again. In addition, TCP considers loss as an indication of network congestion, and reacts by reducing its sending rate. Different versions of TCP implement different ways of adjusting this sending rate. This means that the characteristics of the set of segments in a TCP connection are not just a function of the source-level behavior of the endpoints. This fact will have profound implications for the validation of our approach to synthetic traffic generation.

Intuitively, demonstrating that synthetic traffic is ``realistic'' must be based on a comparison of the statistical properties of real and synthetic traffic. If these properties are reasonably approximated, we can argue with confidence that the traffic generation method and its underlying statistical model provide an adequate foundation for experimental networking research. The comparison can be performed at two levels. First, we can compare source-level properties using the a-b-t modeling approach (see for example section 3.5). Second, we can compare network-level properties, i.e., properties of the actual segments that make up individual connections in real and generated traffic. The material in this chapter is concerned with developing methods for making this latter comparison meaningful.

Since network conditions have an important impact on TCP connections, comparing real and synthetic traffic at the network-level is difficult if network conditions are not incorporated to some extent into the traffic generation system. For example, if we generate traffic that is intended to resemble that of some real link, and connections on this link experience substantial loss rates, the characteristics of the synthetic traffic would be rather different if the synthetic traffic did not experience comparable loss rates. Otherwise, the synthetic traffic would experience higher transfer rates, shorter durations, etc. The first part of this chapter considers methods for characterizing three important, and perhaps the dominant, network-level properties of TCP connections: round-trip times, receiver window sizes, and loss rates. These three properties will be incorporated in our traffic generation method as input parameters, and will make synthetic traffic more comparable to real traffic. Additionally, we also examine the properties of a number of real traces to illustrate the wide range of network conditions in which TCP operates, and how this range changes from one network link to another.

The second part of the chapter considers the actual problem of comparing traffic at the network-level. The research literature has identified a number of statistical properties of traffic that can serve as metrics for assessing the realism of synthetic traffic. We describe these properties and consider their application in the context of comparing traffic traces. We also examine a number of real traces in light of these metrics. Our analysis reveals important differences between the traces, and uncovers some dependencies between network-level metrics and types of source-level behavior.



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Doctoral Dissertation: Generation and Validation of Empirically-Derived TCP Application Workloads
© 2006 Félix Hernández-Campos