Energy-saving analysis of Cloud workload based on K-means clustering

机译：基于K-means聚类的云工作负载节能分析

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摘要

With the development of cloud infrastructure services, IaaS(Infrastructure as a Service) study on energy-saving technology has been attracted more and more attention. IaaS platform providers can provide high performance service for the users. Meanwhile, how to save the energy cost of the cloud platform must be considered without violating the Service Level Agreement(SLA). The overload and underload are two running statuses of physical machine(PM), the former will cause the possibility of SLA violation, while the latter will cause the low utilization rate of PM's resources, causing additional energy consumption. This paper proposes a model of workload characteristic based on K-means clustering analysis, using Google workload trace data set, which is the basis of virtual machine(VM) migrating when PM has been underloading or overloading. The establishment of workload characteristic model can present the demand of system resources in real time so that VM scheduling strategies carry out efficiently.

机译：随着云基础设施服务的发展，有关节能技术的IaaS（基础设施即服务）研究已引起越来越多的关注。 IaaS平台提供商可以为用户提供高性能的服务。同时，必须考虑如何节省云平台的能源成本，而不违反服务水平协议（SLA）。过载和欠载是物理机器（PM）的两种运行状态，前者将导致违反SLA的可能性，而后者将导致PM资源的利用率低下，从而导致额外的能耗。本文利用Google工作负载跟踪数据集，提出了一种基于K-means聚类分析的工作负载特征模型，该模型是PM负载不足或超载时迁移虚拟机的基础。工作量特征模型的建立可以实时呈现系统资源需求，从而有效地执行VM调度策略。

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来源
《2014 IEEE Computing, Communications and IT Applications Conference》|2014年|305-309|共5页
会议地点 Beijing(CN)
作者
Qingxin Xia; Yuqing Lan; Liang Zhao; Limin Xiao;
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作者单位

Sch. of Comput. Sci. Eng., Beihang Univ., Beijing, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
cloud computing; pattern clustering; power aware computing; scheduling; virtual machines; Google workload trace data set; IaaS platform; PM; SLA violation; VM scheduling strategies; cloud infrastructure services; cloud workload; energy consumption; energy cost savïng; energy-saving analysis; energy-saving technology; high performance service; infrastructure as a service; k-means clustering; physical machine; service level agreement; virtual machine; workload characteristic model; Analytical models; Cloud computing; Clu;

机译：云计算;模式集群;功率感知计算;调度;虚拟机; Google工作负载跟踪数据集; IaaS平台; PM; SLA违规; VM调度策略;云基础设施服务;云工作负载;能源消耗;能源成本节省;节能分析;节能技术;高性能服务;基础设施即服务; k-means聚类;物理机;服务水平协议;虚拟机;工作量特征模型;分析模型;云计算; Clu;

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Energy-saving analysis of Cloud workload based on K-means clustering

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