Learning-Based Data Envelopment Analysis for External Cloud Resource Allocation

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题名:	Learning-Based Data Envelopment Analysis for External Cloud Resource Allocation
作者:	施國琛;Cho, Hsin-Hung;Lai, Chin-Feng;Shih, Timothy K.;Chao, Han-Chieh
贡献者:	資訊電機學院資訊工程學系
关键词:	Allocations;Analysis;Cloud computing;Clouds;Communications Engineering;Computational efficiency;Computer centers;Computer Communication Networks;Computer engineering;Computer science;Data envelopment analysis;Electrical Engineering;Energy consumption;Engineering;Information science;IT in Business;Linear programming;Mathematical analysis;Mathematical models;Networks;Operations research;Resource allocation;Servers;Simulation;Software services;Studies;Websites;Wireless networks
日期:	2016-10-01
上传时间:	2026-04-23 13:50:28 (UTC+8)
出版者:	Springer Netherlands;New York: Springer US
摘要:	摘要： A mature cloud system needs a complete resource allocation policy which includes internal and external allocation. They not only enable users to have better experiences, but also allows the cloud provider to cut costs. In the other words, internal and external allocation are indispensable since a combination of them is only a total solution for whole cloud system. In this paper, we clearly explain the difference between internal allocation (IA) and external allocation (EA) as well as defining the explicit IA and EA problem for the follow up research. Although many researchers have proposed resource allocation methods, they are just based on subjective observations which lead to an imbalance of the overall cloud architecture, and cloud computing resources to operate se-quentially. In order to avoid an imbalanced situation, in previous work, we proposed Data Envelopment Analysis (DEA) to solve this problem; it considers all of a user’s demands to evaluate the overall cloud parameters. However, although DEA can provide a higher quality solution, it requires more time. So we use the Q-learning and Data Envelopment Analysis (DEA) to solve the imbalance problem and reduce computing time. As our simulation results show, the proposed DEA+Qlearning will provide almost best quality but too much calculating time. 其他題名： Mobile Netw Appl 出版者： New York: Springer US 出版日期： 2016-10-01 出處： Mobile networks and applications, 2016-10, Vol.21 (5), p.846-855 資源來源： ABI/INFORM Collection 版權： Springer Science+Business Media New York 2016 識別號： ISSN: 1383-469X 識別號： EISSN: 1572-8153 識別號： DOI: 10.1007/s11036-016-0728-2
显示于类别:	[資訊工程學系] 期刊論文

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