Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by selecting the correct number of machines of each type. This paper presents an efficient algorithm to find the best set of computing resources to allocate to the workload. This research is applicable to users provisioning cloud computing resources and to data center owners making purchasing decisions about physical hardware. Studies have shown that cloud computing machines have measurable variability in their performance. Some of the causes of performance variability include small changes in architecture, location within the datacenter, and neighboring applications consuming shared network resources. The proposed algorithm models the uncertainty in the computing resources and the variability in the tasks in a many-task computing environment to find a robust number of machines of each type necessary to process the workload. In addition, reward rate, cost, failure rate, and power consumption can be optimized, as desired, to compute Pareto fronts.

中文翻译：

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使用多目标随机规划的稳态模型实现稳健的基于性能的资源供应

云计算为高性能计算提供了全新的商业模式。拥有一台专用的本地高性能计算机仍然是一些人的选择，但更多人正在转向云计算资源来满足他们的高性能计算需求。通过云计算，可以通过选择每种类型的正确数量的机器来定制您的计算基础架构，以便为您的特定类型的工作负载提供最佳性能。本文提出了一种有效的算法来找到分配给工作负载的最佳计算资源集。这项研究适用于提供云计算资源的用户以及对物理硬件做出购买决策的数据中心所有者。研究表明，云计算机器的性能具有可测量的可变性。性能可变性的一些原因包括架构、数据中心内的位置以及消耗共享网络资源的相邻应用程序的微小变化。所提出的算法对计算资源的不确定性和多任务计算环境中任务的可变性进行建模，以找到处理工作负载所需的每种类型的机器数量。此外，可以根据需要优化奖励率、成本、故障率和功耗，以计算帕累托前沿。所提出的算法对计算资源的不确定性和多任务计算环境中任务的可变性进行建模，以找到处理工作负载所需的每种类型的机器数量。此外，可以根据需要优化奖励率、成本、故障率和功耗，以计算帕累托前沿。所提出的算法对计算资源的不确定性和多任务计算环境中任务的可变性进行建模，以找到处理工作负载所需的每种类型的机器数量。此外，可以根据需要优化奖励率、成本、故障率和功耗，以计算帕累托前沿。