We investigate the tail probabilities of a multiplexer driven by /spl alpha/-stable self-similar traffic. We consider a parsimonious 4-parameter traffic model, which best captures the long range dependence and heavy-tails of aggregate packet traffic in broadband networks. Input traffic with these characteristics, induces buffer dynamics which are qualitatively different from those which arise in traditional traffic management. Using the effective bandwidth theory, we extend the recent results on /spl alpha/-stable self similar-driven queues with infinite buffer to the finite buffer case that model routers/switches more accurately. Queuing simulations with real broadband traffic emphasise the improvements in engineering considerations (e.g., connection admission control, buffer management, statistical multiplexing gains), with respect to the existing results so far. Our experiments involve a large set of real broadband network traffic which consists of Ethernet LAN, Internet WAN and MPEG-1 compressed video traces.

A single fault in a large communication network may result in a large number of fault indications (alarms) making the isolation of the primary source of failure a difficult task. The problem becomes worse in cases of multiple faults. In this paper we present an approach for modelling the problem of fault diagnosis. We propose a graph based network model that takes into account the dependencies among the different objects in the telecommunication environment and a novel approach to estimate the domain of an alarm. Based on that model, we design an algorithm for fault diagnosis and analyze its performance with respect to the accuracy of the fault hypotheses it provides. We also propose and analyze a fault diagnosis algorithm suitable for systems for which an independent failure assumption is valid. Finally, we examine the importance of the information of dependency between objects for the fault diagnosis process.