A New Quality Chart to Improve the Efficiency of Cloud Resource Management

Chen-Fang Tsai, Shin-Li Lu, Jen-Hsiang Chen
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Abstract

This study attempts to upgrade the service efficiencies of cloud management by proposing different quality approaches. We design two detecting mechanisms to search the early violation signal by a different quality level of confidence. This research attempts to prevent assignable variables existed in violation situations. We proposed and verified the Moving Range Chart and EWMA Chart by utilizing CLOUDSIM experiments.
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一种提高云资源管理效率的新质量图
本研究试图通过提出不同的质量方法来提升云管理的服务效率。我们设计了两种检测机制,通过不同的置信度来搜索早期的违规信号。本研究试图防止可赋值变量在违规情况下的存在。我们利用CLOUDSIM实验提出并验证了移动距离图和EWMA图。
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