The Protocol Stack and Its Modulating Effect on Self‐Similar Traffic
Ki‐Hong Park, Gitae Kim, Mark E. Crovella · 2000
Recent measurements of local-area and wide-area traffic have shown that network traffic exhibits variability at a wide range of scales. Such scale-invariant variability is in strong contrast to traditional models of network traffic, which show variability at short scales but are essentially smooth at large time scales; that is, they lack long-range dependence. Since self-similarity is believed to have a significant impact on network performance, understanding the causes and effects of traffic self-similarity is an important problem. In this chapter, we study a mechanism that induces self-similarity in network traffic. We show that self-similar traffic can arise from a simple, high-level property of the overall system: the heavy-tailed distribution of file sizes being transferred over the network. We show that if the distribution of file sizes is heavy tailed—meaning that the distribution behaves like a power law thus generating very large file transfers with nonnegligible probability—then the superposition of many file transfers in a client/server network environment induces self-similar traffic, and this causal mechanism is robust with respect to changes in network resources (bottleneck bandwidth and buffer capacity), topology, interference from cross-traffic with dissimilar traffic characteristics, and changes in the distribution of file request interarrival times. Properties of the transport/network layer in the protocol stack are shown to play an important role in mediating this causal relationship. The mechanism we propose is motivated by the on/off model. The on/off model shows that self-similarity can arise in an idealized context. The chapter is organized as follows. We discuss related work, the network model, and the simulation setup. This is followed by the main section, which explores the effect of file size distribution on traffic self-similarity, including the role of the protocol stack, heavy-tailed versus non-heavy-tailed interarrival time distribution, resource variations, and traffic mixing. We conclude with a discussion of the effect of traffic self-similarity from a performance evaluation perspective, showing its quantitative and qualitative effects with respect to performance measures when both the degree of self-similarity and network resources are varied.