Overview

Chapter 2 presents a review of the state-of-the-art in synthetic traffic generation. We first expand our discussion of packet-level traffic generation and data acquisition, and then examine source-level traffic generation more in depth. We review the literature on application-specific modeling, discussing models of web traffic and other applications, and also consider several approaches for generating traffic driven by more than one application. We also discuss existing methods for controlling the traffic load created in networking experiments. The chapter finally considers some research efforts addressing implementation issues.

Chapter 3 discusses abstract source-level modeling, presenting several examples of real applications and how their behavior can be described using our a-b-t notation. We also present our measurement algorithm for transforming a packet header trace into a collection of sequential and concurrent a-b-t connection vectors. The chapter also includes a validation of the measurement method using synthetic applications, and a measurement study that examines the statistical properties of the a-b-t connection vectors extracted from five real traces.

Chapter 4 focuses on network-level measurement. We first describe our methods for measuring round-trip times, window sizes and loss rates, and an evaluation of their accuracy. While this set of parameters is by no means complete, it does include the main parameters that affect the average throughput of a TCP connection found in a trace. The second part of Chapter 4 describes the network-level metrics that we consider in the evaluation of our traffic generation method: packet and byte throughput time series, their marginal distributions, wavelet spectra, Hurst parameter estimates and time series of active connections.

Chapter 5 describes source-level trace replay and our implementation in a network testbed. We present a validation of this implementation using the source-level trace replays of five traces. For each trace, we study the a-b-t connection vectors extracted from the original traces and those found in replays with and without packet losses at the network links. The results demonstrate the accuracy of our approach, and also uncover some difficulties, which are in some cases inherent to the a-b-t model and its passive method of data acquisition.

Chapter 6 examines the results of several source-level trace replay experiments. Our analysis compares original traces and their source-level trace replays using the rich set of metrics introduced in Chapter 4, revealing a remarkably close approximation. This study also includes a comparison of traffic generated with the a-b-t model and with a simplified version that ``disables'' source-level modeling, which is shown to perform well for some metrics and poorly for others. As in the previous chapter, we also consider experiments with and without artificial losses, showing that loss did not have a dominant impact on the characteristics of the original traffic. In general, our results provide a strong justification of our source-level modeling approach, demonstrating that the closed-loop replay of a-b-t connection vectors closely resembles real traffic.

Chapter 7 presents our two resampling methods, Poisson Resampling and Block Resampling. These methods enable the researcher to introduce controlled variability in source-level trace replay experiments, without sacrificing reproducibility. In addition, we consider the problem of load scaling, i.e., how to control the resampling process to obtain a new trace with a target offered load. Our work demonstrates that this task can be accomplished by keeping track of the total number of data bytes in the resampled trace, but not by keeping track of the number of connections. Our scaling methods eliminate the common need for running a preliminary study to calibrate the traffic generator.

Chapter 8 presents our conclusions and discusses future work.