c © Copyright by Guanghui He, 2004 EXPLOITATION OF LONG-RANGE DEPENDENCE IN INTERNET TRAFFIC FOR RESOURCE AND TRAFFIC MANAGEMENT BY

A long-held belief regarding the scaling behavior—self-similar or Long-Range Dependence (LRD) of Internet traffic is that the scaling behavior has adverse impacts on the Internet. In particular, it causes the queue length tend to infinity and hence severe packet drops. The scaling property means much more bursty traffic which consists of concentrated periods of high activity and low activity at a wide range of time scales, and this burstiness adversely affects resource management and degrades the overall Internet performance. In this thesis we study the scaling behavior of the Internet traffic from a totally different per-spective, i.e., we aim to take advantage of the self-similar/LRD property of the Internet traffic for the sake of resource and traffic control. Since self-similar/LRD implies the existence of nontrivial correlation structure at any time scales, accurate prediction can be faithfully achieved and the prediction results can be used to do resource and traffic control. In light of this, first, in order to make a thorough understanding of the scaling behavior, we propose a hierarchical model that has an one-on-one correspondence to the protocols in the protocol hierarchy of IP networks and give insights on how, and to what extent, the user/protocol behavior in each protocol layer contribute