Approximation method for probability distribution functions using Cox distribution to evaluate multimedia systems

Sasaki, Yukie; Imai, Hiroo; Tsunoyama, Masahiro; Ishii, Ikuo

doi:10.1109/prdc.2001.992717

Cited by 3 publications

(2 citation statements)

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Section: Phase Type Services and Dropping Policiesmentioning

confidence: 99%

“…The Cox distribution is known to form a dense subset within the set of all distributions with real, non-negative support. Further, algorithms have been provided[16] to approximate an arbitrary distribution with a Cox distribution, and examples were given for both light-tailed and heavy-tailed distributions.Dropping is possible in a stochastic capacity system. Now we discuss the dropping rules (i.e., which victim to choose when the system is forced to drop).…”

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confidence: 99%

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Performance implications of fluctuating server capacity

Luo

Williamson

2008

Computer Communications

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In this paper, we consider a variant of an M/M/c/c loss system with fluctuating server capacity. Given a set of primary inputs, such as arrival rate, service rate, and capacity fluctuation rate, we develop a detailed model using MRGP, such that blocking and dropping metrics can be calculated explicitly.To gain insight into performance implications of a stochastic capacity queue, we conduct an analysis on a simple, approximate model. We investigate the functional behaviour of the system, using both the approximate and the detailed model. The significance of results in the context of network performance evaluation and capacity planning is highlighted.

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Section: Phase Type Services and Dropping Policiesmentioning

confidence: 99%

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confidence: 99%