Loss Analysis for Networks based on Heavy-Tailed and Self-Similar Traffic

Abstract

Abstract Many businesses on the computer network appear heavy-tailed self-similarity (long range dependency), which means network traffic exists burst. Various service source with burst characteristic which show self-similar have a significant effect on transmission performance, network traffic control strategy and network performance indicators such as loss. Loss is an important QoS parameter at the network node, which need to be considered and controlled in all types of traffic, but there is no paper study loss analysis based on heavy-tailed self-similarity. Thus, only analyze and evaluate loss under self-similar traffic can reduce the adverse effects which caused by traffic self-similarity and optimize network performance. We adopt stochastic network calculus approach to bewrite loss lssue. Based on this, we present the loss analysis in the case of a single node network using heavy-tailed service curve and heavy-tailed self-similar arrival curve, and loss analysis under cross traffic, multi-node networks with concatenation. And we also get the relational graph between the loss and the arrival rate, service rate.