Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing
Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption opti...
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| Vydané v: | Future generation computer systems Ročník 124; s. 12 - 20 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.11.2021
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.
•An effective agile response optimization model.•A novel task scheduling optimization model.•A strong agile response task scheduling optimization algorithm. |
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| AbstractList | Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.
•An effective agile response optimization model.•A novel task scheduling optimization model.•A strong agile response task scheduling optimization algorithm. |
| Author | Shu, Wanneng Xiong, Neal Naixue Cai, Ken |
| Author_xml | – sequence: 1 givenname: Wanneng surname: Shu fullname: Shu, Wanneng email: shuwanneng@whu.edu.cn organization: College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China – sequence: 2 givenname: Ken orcidid: 0000-0002-7334-1741 surname: Cai fullname: Cai, Ken email: icken@zhku.edu.cn organization: College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China – sequence: 3 givenname: Neal Naixue surname: Xiong fullname: Xiong, Neal Naixue email: xiongnaixue@gmail.com organization: Department of Mathematics and Computer Science, Northeastern State University, OK 74464, USA |
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| Keywords | Task scheduling Agile response Cloud server resource pool Virtualization technology Green cloud computing |
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