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BURSE: A Bursty and Self-Similar Workload Generator for Cloud Computing

机译:BURSE:突发且自相似的云计算工作负载生成器

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摘要

As two of the most important characteristics of workloads, burstiness and self-similarity are gaining more and more attention. Workload generation, which is a key technique for performance analysis and simulations, has also attracted an increasing interest in cloud community in recent years. Though a large number of methods for synthetically generating bursty or self-similar workloads have been proposed in the literature, none of them can deal with workload generation with both of the two characteristics. In this paper, a configurable and intelligible synthetic generator (BURSE) is proposed for bursty and self-similar workloads in cloud computing based on a superposition of two-state Markov Modulated Poisson Processes (MMPP2s). The proposed generator can produce workloads with both specified intension of burstiness and self-similarity. Detailed experimental evaluation demonstrates the accuracy, robustness and good applicability of BURSE.
机译:作为工作负载的两个最重要特征,突发性和自相似性越来越受到关注。工作负载生成是性能分析和仿真的关键技术,近年来,它也引起了人们对云社区的越来越多的关注。尽管在文献中已经提出了大量用于合成突发性或自相似工作量的方法,但是它们都不能处理具有这两个特征的工作量生成。本文基于两态马尔可夫调制泊松过程(MMPP2s)的叠加,针对云计算中突发性和自相似的工作量,提出了一种可配置且可理解的合成发生器(BURSE)。拟议的生成器可以产生具有指定突发性和自相似性的工作负载。详细的实验评估证明了BURSE的准确性,鲁棒性和良好的适用性。

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