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$\textstyle \parbox{0.2in}{\mbox{}}$$\textstyle \parbox{5.25in}{\begin{singlespace}\textit{As far as the laws of mat...
...nd as far as they are certain, they do not refer to reality.}\end{singlespace}}$$\textstyle \parbox{0.2in}{\mbox{}}$
$\textstyle \parbox{5.25in}{\begin{flushright}--- \textsc{{Albert Einstein (1879--1955)}}\end{flushright}}$

$\textstyle \parbox{0.2in}{\mbox{}}$$\textstyle \parbox{5.25in}{\begin{singlespace}\textit{Humankind cannot stand very much reality.}\end{singlespace}}$$\textstyle \parbox{0.2in}{\mbox{}}$
$\textstyle \parbox{5.25in}{\begin{flushright}--- \textsc{{T. S. Elliot (1888--1965)}}\end{flushright}}$

Research in networking has to deal with the extreme complexity of many layers of technology interacting with each other in frequently unexpected ways. As a consequence, there is a broad consensus among researchers that purely theoretical analysis is not enough to demonstrate the effectiveness of network technologies. More often than not, careful experimentation in simulators and network testbeds under controlled conditions is needed to validate new ideas. Every researcher therefore faces, at some point or another, the need to design realistic networking experiments, and synthetic network traffic is a foremost element of these experiments. Synthetic network traffic represents not only the workload of a computer network, but also the direct or indirect target of any optimization. For instance, congestion control research focuses on preserving as much as possible the ability of a network to transfer data in the face of overload. Therefore, evaluating a new congestion control mechanism in a transport protocol such as the [Pos81] usually requires constructing experiments in which a number of network hosts exchange data using this protocol in an environment with one or more saturated links. The value of the new mechanism is then expressed as a function of the performance of these data exchanges. For example, the new mechanism may be optimized for achieving a higher overall throughput or a more fair allocation of bandwidth.

A fundamental insight, which provides the main motivation for this dissertation, is that the characteristics of synthetic traffic have a dramatic impact on the outcome of networking experiments. For example, a new mechanism that improves the throughput of bulk, long-lasting file transfers in a congested environment may not improve and may even degrade the response time of the small data exchanges in web traffic. This was precisely the case of , an mechanism. The original analysis by Floyd and Jacobson [FJ93a] clearly demonstrated the benefits of RED over the basic queuing mechanism for bulk transfers. In this study, RED queues were exposed to a small number (2-4) of large file transfers. However, a later experimental study by Christiansen et al. [CJOS00] showed that this first AQM mechanism degraded the performance of web traffic in highly congested environments. In contrast to the original evaluation, web traffic mostly consists of a very large number of small data transfers, which create a very different workload. The emergence of the web clearly changed the nature of Internet traffic, and made it necessary to revisit existing results obtained under different workloads. The systematic evaluation of network mechanisms must therefore include experiments covering the wide range of traffic characteristics observed on Internet links. It is critical to provide the research community with methods and tools for generating synthetic traffic as representative as possible of this range of characteristics.

The concept of source-level modeling introduced by Paxson and Floyd [PF95] constitutes a major influence on this dissertation. These authors advocated for building models of the behavior of Internet applications (i.e., the sources of Internet traffic), and generating traffic in networking experiments by driving network stacks with these application models. The main benefit of this approach is that traffic is generated in a closed-loop manner, which fully preserves the fundamental feedback loop between network endpoints and network characteristics. For example, a model of web traffic can be used to generate traffic using TCP/IP network stacks, and the generated traffic will properly react to different levels of congestion in networking experiments. In contrast, open-loop traffic generation is associated to models of the packet arrivals on network links, and these models are insensitive to changes in network conditions, and tied to the original conditions under which they were developed. This makes them inappropriate for experimental studies that change these conditions.

The main motivation of our work is to address one important difficulty with source-level modeling. In the past, source-level modeling has been associated with characterizing the behavior of individual applications. While this approach can result in high-quality models, it is a difficult process that requires a large amount of effort. As a consequence, only a small number of models is available, and they are often outdated. This is in sharp contrast to the traffic observed in most Internet links, which is driven by rich traffic mixes composed of a large number of applications. Source-level modeling of individual applications does not scale to modern traffic mixes, making it very problematic for networking researchers to conduct representative experiments with closed-loop traffic.

This dissertation presents a new methodology for generating network traffic in testbed experiments and software simulations. We make three main contributions. First, we develop a new source-level model of network traffic, the a-b-t model, for describing in a generic and intuitive manner the behavior of the applications driving TCP connections. Given a packet header trace collected at an arbitrary Internet link, we use this model to describe each TCP connection in the trace in terms of data exchanges and quiet times, without any knowledge of the actual semantics of the application. Our algorithms make it possible to efficiently derive empirical characterizations of network traffic, reducing modeling times from months to hours. The same analysis can be used to incorporate network-level parameters, such as round-trip times, to the description of each connection, providing a solid foundation for traffic generation. Second, we propose a traffic generation method, source-level trace replay, where traffic is generated by replaying the observed behavior of the applications as sources of traffic. This is therefore a method for generating entire traffic mixes in a closed-loop manner. One crucial benefit of our method is that it can be evaluated by directly comparing an original trace and its source-level replay. This makes it possible to systematically study the realism of synthetic traffic, in the terms of how well our description of the connections in the original traffic mix reflects the nature of the original traffic. In addition, this kind of comparison provides a means to understand the impact that the different characteristics of a traffic mix have on specific traces and on Internet traffic in general. Third, we propose and study two approaches for introducing variability in the generation process and scaling (up or down) the level of traffic load in the experiments. These operations greatly increase the flexibility of our approach, enabling a wide range of experimental investigations conducted using our traffic generation method.

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Next: Abstract Source-Level Modeling Up: diss Previous: List of Figures   Contents

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