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<p>Perfect/exact sampling has been used in situations, where the underlying distribution from which random samples are of interest is unknown or complex, for more than three decades now. However, to apply perfect sampling techniques to specific models, additional techniques are required. In this paper, the perfect sampling technique is adapted to study a workload process as well as the per-class waiting times of customers in a computing cluster model (also known as the multiserver job model) by using stochastic comparison to a single-server queue, for the first time. Numerical experiments demonstrate the effectiveness of the method in scenarios when the service times have heavy-tailed (Pareto), phase-type, and exponential distributions.</p>

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Regenerative exact simulation for multiserver job model

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Alexander Golovin,
  • Alexander Rumyantsev,
  • Srinivas Chakravarthy

摘要

Perfect/exact sampling has been used in situations, where the underlying distribution from which random samples are of interest is unknown or complex, for more than three decades now. However, to apply perfect sampling techniques to specific models, additional techniques are required. In this paper, the perfect sampling technique is adapted to study a workload process as well as the per-class waiting times of customers in a computing cluster model (also known as the multiserver job model) by using stochastic comparison to a single-server queue, for the first time. Numerical experiments demonstrate the effectiveness of the method in scenarios when the service times have heavy-tailed (Pareto), phase-type, and exponential distributions.