arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.04860cs.DC
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云计算中基于VM整合的节能算法比较与评价

Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

Qiheng Zhou, Minxian Xu, Sukhpal Singh Gill, Chengxi Gao, Wenhong Tian, Chengzhong Xu, Rajkumar Buyya

摘要:

云计算范式已经彻底改变了IT行业,并能够提供计算作为第五公用事业。云计算采用现收现付的模式,能够随时为客户动态提供资源。云计算被视为现代经济的支柱之一,受到学术界和工业界的关注。然而,云数据中心的高能耗导致了高运营成本和对环境的碳排放。因此,需要绿色云计算来确保能源效率和可持续性,这可以通过节能技术来实现。其中一种主要方法是应用节能算法来优化资源使用和能耗。目前,已经提出了各种基于虚拟机整合的节能算法来降低云计算环境的能量。然而,它们中的大多数没有在相同的场景下进行全面的比较,并且它们的性能没有用相同的实验设置进行评估。这使得用户很难为他们的目标选择合适的算法。为了对现有的节能算法提供见解,并帮助研究人员选择最合适的算法,本文从多个角度,包括架构,建模和度量,深入比较了几种最先进的节能算法。此外,我们还实现和评估这些算法与CloudSim工具包中相同的实验设置。实验结果表明,这些算法的性能比较与综合的结果。最后,这些算法的详细讨论。

英文摘要:

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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