Fast and physically-based generation of self-similar network traffic with applications to ATM performance evaluation

Self-similarity concepts relate statistical properties of processes observed at different time scales through judicious scaling of time and space.They have recently been shown to be ideally suited to account for the surprising scaling properties that measured network traffic (e.g., number of packets/bytes per time unit) exhibits over a wide range of time scales, from milliseconds to seconds to minutes and beyond.The observed self-similar property in measurements from working packet networks is in sharp contrast to commonly made assumptions about the bursty nature of network traffic and challenges many of the traditional approaches to traffic and performance modeling.In this paper, we illustrate how the self-similar finding gives rise to new mathematical results that (i) clear the way for physically-based approaches to network traffic modeling, (ii) can be combined with high-performance computing capabilities to yield new and fast (i.e., linear in the number of observations) methods for generating self-similar traces, and (iii) provide new insights into the potential performance implications that self-similar traffic can have on the design of network equipment and on the perceived quality-of-service experienced by some of the dominant applications and services.In particular, studying the cell loss dynamics (rather than the traditional long-term cell loss rate) observed at an ATM switch that is fed by self-similar traffic, we discuss the impact of network traffic self-similarity on broadband services such as VBR video and on popular network protocols such as TCP/IP.