Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme
As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6646881 |
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| author | Yu Cui Shunfu Jin Wuyi Yue Yutaka Takahashi |
| author_facet | Yu Cui Shunfu Jin Wuyi Yue Yutaka Takahashi |
| author_sort | Yu Cui |
| collection | DOAJ |
| description | As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter. |
| format | Article |
| id | doaj-art-44d211d7240144cb9c71a50add830f3e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-44d211d7240144cb9c71a50add830f3e2025-02-03T06:07:36ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66468816646881Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management SchemeYu Cui0Shunfu Jin1Wuyi Yue2Yutaka Takahashi3School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaDepartment of Intelligence and Informatics, Konan University, Kobe 658-8501, JapanGraduate School of Informatics, Kyoto University, Kyoto 606-8501, JapanAs an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.http://dx.doi.org/10.1155/2021/6646881 |
| spellingShingle | Yu Cui Shunfu Jin Wuyi Yue Yutaka Takahashi Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme Complexity |
| title | Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme |
| title_full | Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme |
| title_fullStr | Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme |
| title_full_unstemmed | Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme |
| title_short | Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme |
| title_sort | performance optimization of cloud data centers with a dynamic energy efficient resource management scheme |
| url | http://dx.doi.org/10.1155/2021/6646881 |
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