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A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation

2002; Elsevier BV; Volume: 47; Issue: 2-3 Linguagem: Inglês

10.1016/s0166-5316(01)00069-4

1872-745X

Andrés Suárez-González, J.C. López-Ardao, Cándido López-Garcı́a, Manuel Fernández‐Veiga, Raúl F. Rodríguez‐Rubio, M.E. Sousa‐Vieira,

Advanced Queuing Theory Analysis

Several traffic measurement reports have convincingly shown the presence of self-similarity at the packet level in current networks, inducing as a result a revolution in the stochastic modeling of traffic. The essence of this behavior can be adequately captured by several classes of self-similar stochastic processes. But the use of these in performance analysis has opened new problems and research issues, also in simulation studies where the efficient generation of synthetic sample paths with self-similar properties is one of the fundamental concerns. In this paper, we present a flexible and efficient generator of self-similar traces, based on a simple M/G/∞ model which uses a new heavy-tailed discrete distribution.