In this article, we consider the problem of selecting appropriate heterogeneous servers in cloud centers for stochastically arriving requests in order to obtain an optimal tradeoff between the expected response time and power consumption. Heterogeneous servers with uncertain setup times are far more common than homogenous ones. The heterogeneity of servers and stochastic requests pose great challenges in relation to the tradeoff between the two conflicting objectives. Using the Markov decision process, the expected response time of requests is analyzed in terms of a given number of available candidate servers. For a given system availability, a binary search method is presented to determine the number of servers selected from the candidates. An iterative improvement method is proposed to determine the best servers to select for the considered objectives. After evaluating the performance of the system parameters on the performance of algorithms using the analysis of variance, the proposed algorithm and three of its variants are compared over a large number of random and real instances. The results indicate that proposed algorithm is much more effective than the other four algorithms within acceptable CPU times.

中文翻译：

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基于排队论的异构云服务器性能分析

在本文中，我们考虑在云中心为随机到达的请求选择合适的异构服务器的问题，以获得预期响应时间和功耗之间的最佳权衡。具有不确定设置时间的异构服务器比同类服务器更常见。服务器的异构性和随机请求对两个相互冲突的目标之间的权衡提出了巨大的挑战。使用马尔可夫决策过程，根据给定数量的可用候选服务器分析请求的预期响应时间。对于给定的系统可用性，提出了一种二进制搜索方法来确定从候选服务器中选择的服务器数量。提出了一种迭代改进方法来确定为考虑的目标选择最佳服务器。在使用方差分析评估系统参数对算法性能的影响后，将所提出的算法及其三个变体在大量随机和真实实例上进行了比较。结果表明，在可接受的CPU时间内，所提出的算法比其他四种算法更有效。