Cloud workflow scheduling algorithm based on novelty ranking and multi-quality of service

被引：0
|
作者：
Yuan Y.-W. [1 ,2 ]
Yu J. [1 ,2 ]
Zheng H.-S. [1 ]
Wang J.-J. [1 ]
机构：
[1] School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou
[2] Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2017年 / 51卷 / 06期
关键词：
Cloud workflow; Novelty ranking; Quality of service(QoS); Recommendation system; Scheduling; Simulated annealing algorithm;
D O I：
10.3785/j.issn.1008-973X.2017.06.017
中图分类号：
学科分类号：
摘要：
A cloud service workflow scheduling algorithm based on novelty ranking and multiple quality of service (QoS) was proposed, aiming at the problem that the optimal scheduling based on user cost and system utilization was not considered in the existing researches. Frequency of the tasks performed by the resource node, waiting time and execution time of the resource node were added into the recommendation model. The simulated annealing algorithm was used to train the recommendation model, and the priority factor was calculated. The scheduler performed the scheduling and updated it according to the priority factor table. Simulation results show that the proposed algorithm is better than the Q-learning algorithm in terms of task execution time, and the combined index of user cost and system utilization is better than that of the Q-learning algorithm on CloudSim platform. © 2017, Zhejiang University Press. All right reserved.
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页码：1190 / 1196
页数：6
相关论文
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