Fitting World-Wide Web Request Traces with the EM-Algorithm

In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies

Информационные технологии в банковской системе

Almost all activities of the Bank subject to the domination systems. The system itself involves a procedure control, a set of interconnected elements, procedures, methods, and many similar concepts. When the Bank is recruiting employees, it applies to this particular system, which involves placing ads on job interviews, the definition of appropriate skills, discussion of working conditions and so on. This process is a slender organized system with its internal procedures and prescribed norms

Moment Matching-Based Distribution Fitting with Generalized Hyper-Erlang Distributions

This paper describes a novel moment matching based fitting method for phase-type (PH) distributions. A special sub-class of phase-type distributions is introduced for the fitting, called generalized hyper-Erlang distributions. The user has to provide only two parameters: the number of moments to match, and the upper bound for the sum of the multiplicities of the eigenvalues of the distribution, which is related to the maximal size of the resulting PH distribution. Given these two parameters, our method obtains all PH distributions that match the target moments and have a Markovian representation up to the given size. From this set of PH distributions the best one can be selected according to any distance function

Fitting World-Wide Web Request Traces with the EM-Algorithm

In recent years, various researchers have shown that network traffic that is due to world-wide web transfers shows characteristics of self-similarity and it has been argued that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods that are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planning purposes.\ud \ud However, heavy-tailed distributions cannot be used so easily for analytical or numerical evaluation studies. To overcome this problem, in this paper, we approximate the empirical distributions by analytically more tractable, that is, hyper-exponential distributions. For that purpose, we present a new fitting algorithm based on the expectation-maximisation and show it to perform well both for pure traffic statistics as well as in queuing studies. \u