Scheduling strategy of compute-intensive task-flow in generalized cluster

被引：0

作者：

Zhang K.-J. ^{[1
,2
]}

Hu Y.-N. ^{[1
,2
]}

Li C.-S. ^{[1
,2
]}

Fu Y. ^{[1
,2
]}

Li P.-C. ^{[1
,2
]}

机构：

[1] College of Computer & Information Technology, Northeast Petroleum University, Daqing

[2] Heilongjiang Provincial Key Laboratory of Oil Big Data & Intelligent Analisys, Daqing

来源：

Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 12期

关键词：

Compute-intensive; Continuous bidding game; Generalized-cluster; Multi-objective optimization; Task scheduling;

D O I：

10.13195/j.kzyjc.2018.1675

中图分类号：

学科分类号：

摘要：

A scheduling strategy based on multi-objective continuous bidding game is proposed to solve the problem that it is difficult to make a quick and reasonable scheduling plan for compute-intensive task-flow in a generalized-cluster with many computing nodes. To ensure the rationality of the optimal solution, a multi-objective optimal scheduling model is established, the dimensions of multi-objective optimization function are reduced, and the multi-objective optimization function is converted into a sum-objective function using the linear weighting method. For improving the search speed of the optimal solution, the ETC matrix is introduced for expressing the form of optimal solution, and continuous bidding game algorithm is designed. By simulating real scenarios and comparing with similar algorithms, it is proved that the scheduling strategy has obvious advantages regarding response speed, resource cost performance and total cost expenditure in the generalized-cluster. © 2019, Editorial Office of Control and Decision. All right reserved.

引用

页码：2537 / 2546

页数：9

共 24 条

[1]

Gonzalez N.M., Miers C.C., Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures, Journal of Cloud Computing, 6, 1, pp. 13-15, (2017)

[2]

Papa G., Comparison of multi-objective approaches to the real-world production scheduling, Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, 48, 3, pp. 457-471, (2018)

[3]

Mahadev S., Edge computing, Computer, 50, 10, pp. 36-38, (2017)

[4]

Shi W., Cao J., Zhang Q., Edge Computing: Vision and challenges, IEEE IoT Journal, 3, 5, pp. 637-646, (2016)

[5]

Zhou Y.W., Yang B., Zhang W.Z., Security access model in pervasive computing environment, Acta Electronica sinica, 45, 4, pp. 959-965, (2017)

[6]

Wang L., Wang J.J., Wu C.G., Research progress of green workshop scheduling optimization, Control and Decision, 33, 3, pp. 385-391, (2018)

[7]

Shen Y., Qin X.L., Bao Z.F., An efficient multi-objective scheduling method for data flow in cloud environment, Journal of software, 28, 3, pp. 579-597, (2017)

[8]

Zheng X., Zhang J., Gao Q., Application of non-cooperative game theory to multi-objective scheduling problem in the automated manufacturing system, International Conference on Automatic Control & Artificial Intelligence, pp. 554-557, (2013)

[9]

Zhang Y., Wang J., Liu S., Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing, International Journal of Intelligent Systems, 32, 4, pp. 934-948, (2016)

[10]

Ni Q., Zarakovitis C.C., Nash bargaining game theoretic scheduling for joint channel and power allocation in cognitive radio systems, IEEE Journal on Selected Areas in Communications, 30, 1, pp. 70-81, (2011)

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