Reliable and Energy Efficient Resource Provisioning and Allocation in Cloud Computing

Yogesh Sharma, B. Javadi, Weisheng Si, Daniel W. Sun
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引用次数: 12

Abstract

Reliability and Energy-efficiency is one of the biggest trade-off challenges confronting cloud service providers. This paper provides a mathematical model of both reliability and energy consumption in cloud computing systems and analyses their interplay. This paper also proposes a formal method to calculate the finishing time of tasks running in a failure prone cloud computing environment using checkpointing and without checkpointing. To achieve the objective of maximizing the reliability and minimizing the energy-consumption of cloud computing systems, three resource provisioning and virtual machine (VM) allocation policies using the aforementioned mathematical models are proposed. These three policies are named Reliability Aware Best Fit Decreasing (RABFD), Energy Aware Best Fit Decreasing (EABFD), Reliability-Energy Aware Best Fit Decreasing (REABFD). A simulation based evaluation of the proposed policies has been done by using real failure traces and workload models. The results of our experiments demonstrated that by considering both reliability and energy factors during resource provisioning and VM allocation, the reliability and energy consumption of the system can be improved by 23% and 61%, respectively.
云计算中可靠和节能的资源供应和分配
可靠性和能源效率是云服务提供商面临的最大权衡挑战之一。本文建立了云计算系统可靠性和能耗的数学模型,并分析了两者的相互作用。本文还提出了一种使用检查点和不使用检查点的形式化方法来计算在易发生故障的云计算环境中运行的任务完成时间。为了实现云计算系统可靠性最大化和能耗最小化的目标,利用上述数学模型提出了三种资源分配和虚拟机分配策略。这三种策略分别被命名为可靠性感知最佳拟合递减(RABFD)、能量感知最佳拟合递减(EABFD)、可靠性-能量感知最佳拟合递减(REABFD)。通过使用真实的故障跟踪和工作负载模型,对所建议的策略进行了基于模拟的评估。实验结果表明,在资源分配和虚拟机分配过程中同时考虑可靠性和能耗因素,系统的可靠性和能耗可分别提高23%和61%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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