Cited By
View all- Pham QNguyen DMirjalili SHoang DNguyen DPathirana PHwang W(2021)Swarm intelligence for next-generation networksJournal of Network and Computer Applications10.1016/j.jnca.2021.103141191:COnline publication date: 1-Oct-2021
A multi-objective optimal scheduling strategy for cloud service based on fluctuation cost
Authors: 
Chen Liu, Shiyong Liu, Yinhua LinAuthors Info & Claims
Pages 211 - 217
Abstract
With the development of cloud computing technology, cloud services put forward higher requirements for Internet bandwidth, traffic, execution time, cost, etc. At the same time, more and more fierce market competition has led to uncontrollable online business, which has resulted in more and more network fluctuations and business fluctuations. These fluctuation risks make cloud services unstable and beyond expectations. Traditional cloud service scheduling algorithms are more based on time, resource balance, execution cost, etc., which are more suitable for stable environment. When there are fluctuations, these algorithms can not take the impact of fluctuations into account, resulting in unreasonable scheduling strategy. In this study, we propose a multi-objective optimization scheduling strategy for cloud service based on fluctuation cost. We use the fluctuation cost to evaluate the potential impact of the fluctuation, construct the resource sequence based on the fluctuation factor, and obtain the fluctuation cost value through the fluctuation cost algorithm. Combined with execution time optimization, task priority and other objectives, particle swarm optimization scheduling algorithm is improved to meet the multi-objective strategy requirements. Finally, the experiment proves that this strategy can better handle the resource scheduling problem when fluctuations occur. By setting different weight values, it can provide more solutions for user decision-making in the actual situation.
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- A multi-objective optimal scheduling strategy for cloud service based on fluctuation cost
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Published In
May 2020
220 pages
ISBN:9781450375344
DOI:10.1145/3393527
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Published: 26 October 2020
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Cited By
View all- Pham QNguyen DMirjalili SHoang DNguyen DPathirana PHwang W(2021)Swarm intelligence for next-generation networksJournal of Network and Computer Applications10.1016/j.jnca.2021.103141191:COnline publication date: 1-Oct-2021
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