Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud
One of the key problems facing cloud applications is to reduce their energy consumption, which can increase the working lifetime of a machine, decrease the operation costs of cloud providers, and the environmental impact caused by power consumption. It is very important to design and evaluate an ene...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8424119/ |
| _version_ | 1831541917501882368 |
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| author | Ping Kuang Wenxia Guo Xiang Xu Hongjian Li Wenhong Tian Rajkumar Buyya |
| author_facet | Ping Kuang Wenxia Guo Xiang Xu Hongjian Li Wenhong Tian Rajkumar Buyya |
| author_sort | Ping Kuang |
| collection | DOAJ |
| description | One of the key problems facing cloud applications is to reduce their energy consumption, which can increase the working lifetime of a machine, decrease the operation costs of cloud providers, and the environmental impact caused by power consumption. It is very important to design and evaluate an energy-efficient cloud. Recently, two open problems are raised in the literature: 1) what is the optimal solution (the lower bound) for the total energy consumption? and 2) what is the energy-efficiency for a scheduling algorithm? In this paper, we consider two major scheduling policies: 1) always power-on physical machines (PMs) once turning-on and 2) turning-off (hibernating) idle PMs, both with possible virtual machine migrations during evaluation. Focusing on compute-intensive applications on cloud, we propose analytical methods to settle down the two open problems. Our theoretical results are validated by experimental results in different scheduling scenarios and can be applied in cloud computing environments to help energy-efficient design. |
| first_indexed | 2024-12-17T00:26:29Z |
| format | Article |
| id | doaj.art-6f5a3037646f45d8955f67d8937f3daa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:26:29Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6f5a3037646f45d8955f67d8937f3daa2022-12-21T22:10:28ZengIEEEIEEE Access2169-35362018-01-016455154552610.1109/ACCESS.2018.28614628424119Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on CloudPing Kuang0Wenxia Guo1Xiang Xu2Hongjian Li3Wenhong Tian4https://orcid.org/0000-0002-5551-9796Rajkumar Buyya5School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Computer Science, The University of Melbourne, Melbourne, AustraliaOne of the key problems facing cloud applications is to reduce their energy consumption, which can increase the working lifetime of a machine, decrease the operation costs of cloud providers, and the environmental impact caused by power consumption. It is very important to design and evaluate an energy-efficient cloud. Recently, two open problems are raised in the literature: 1) what is the optimal solution (the lower bound) for the total energy consumption? and 2) what is the energy-efficiency for a scheduling algorithm? In this paper, we consider two major scheduling policies: 1) always power-on physical machines (PMs) once turning-on and 2) turning-off (hibernating) idle PMs, both with possible virtual machine migrations during evaluation. Focusing on compute-intensive applications on cloud, we propose analytical methods to settle down the two open problems. Our theoretical results are validated by experimental results in different scheduling scenarios and can be applied in cloud computing environments to help energy-efficient design.https://ieeexplore.ieee.org/document/8424119/Cloud data centersenergy-aware resource schedulingthe lower boundenergy efficiencymodified interval scheduling |
| spellingShingle | Ping Kuang Wenxia Guo Xiang Xu Hongjian Li Wenhong Tian Rajkumar Buyya Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud IEEE Access Cloud data centers energy-aware resource scheduling the lower bound energy efficiency modified interval scheduling |
| title | Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud |
| title_full | Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud |
| title_fullStr | Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud |
| title_full_unstemmed | Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud |
| title_short | Analyzing Energy-Efficiency of Two Scheduling Policies in Compute-Intensive Applications on Cloud |
| title_sort | analyzing energy efficiency of two scheduling policies in compute intensive applications on cloud |
| topic | Cloud data centers energy-aware resource scheduling the lower bound energy efficiency modified interval scheduling |
| url | https://ieeexplore.ieee.org/document/8424119/ |
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