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Service Computing     
Multi-objective hybrid cloud task scheduling using twice clustering
LI Jian-li, DING Ding, LI Tao
Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
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Abstract  A twice clustering method was introduced aiming at the case that hybrid cloud environment contains a large number of heterogeneous computing nodes. This method reduced task search space through clustering the heterogeneous resources based on the synthetic characteristics of resources and splitting tasks to the appropriate cluster resources. On this basis, multi-objective task scheduling algorithm in hybrid cloud was proposed, combined with the security and reliability of the private cloud, the scalability of the public cloud and the diversity of user requirements. Firstly, the earliest deadline first algorithm was used in private cloud. To accomplish more tasks, the task was assigned to resource whose completing time was closest to the deadline for each cluster. Then, the public cloud handled the overloading tasks, which would choose the lowest-cost resource under the constraint of computing budget, communication cost and the deadline. Simulation results confirm that the proposed algorithm performs better in lower cost, better resource utilization and greater user satisfaction, compared to the traditional algorithm without clustering.

Published: 11 June 2017
CLC:  TP 393  
Cite this article:

LI Jian-li, DING Ding, LI Tao. Multi-objective hybrid cloud task scheduling using twice clustering. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1233-1241.



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