A highly efficient M/G/∞ model for generating self-similar traces
M.E. Sousa‐Vieira, Andrés Suárez-González, Cándido López-Garcı́a, Manuel Fernández‐Veiga, J.C. López-Ardao
Abstract
M.E. Sousa‐Vieira, Andrés Suárez-González, Cándido López-Garcı́a, Manuel Fernández‐Veiga, J.C. López-Ardao
Abstract
Several traffic measurement reports have convincingly shown the presence of self-similarity in modern networks, inducing as a result a revolution in the stochastic modeling of traffic. The use of self-similar processes in performance analysis has opened new problems and research issues in simulation studies, where the efficient generation of synthetic sample paths with self-similar properties is one of the fundamental concerns. We present an M/G//spl infin/ generator of self-similar traces, based on a highly efficient simulation model using the decomposition property of Poisson processes.
OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Several traffic measurement reports have convincingly shown the presence of self-similarity in modern networks, inducing as a result a revolution in the stochastic modeling of traffic. The use of self-similar processes in performance analysis has opened new problems and research issues in simulation studies, where the efficient generation of synthetic sample paths with self-similar properties is one of the fundamental concerns. We present an M/G//spl infin/ generator of self-similar traces, based on a highly efficient simulation model using the decomposition property of Poisson processes.
Key concepts: Generator (circuit theory), Self-similarity, Computer science, Property (philosophy), Decomposition, Sample (material), Theoretical computer science, Poisson distribution