跨地域分布数据中心高成本效益的任务调度

doi:10.3969/j.issn.0255-8297.2019.06.011

Abstract

Abstract: In this paper, we study the cost minimization for cloud users in geo-distributed cloud systems. By modeling it as a general assignment problem (GAP), we use the augmented Lagrangian multiplier method (ALMM) to obtain the optimal schedule solution. We additionally apply an Adjusting algorithm that adjusts the solution produced by ALMM to make it more feasible. We further use a decreased value density scheduling algorithm (DVDS) to speed up the convergence of ALMM. Experimental results show that DVDS algorithm can work out solutions in a much shorter period than ALMM does with costs similar to ALMM's in the case of small task, and only 10% more in the case of large task.

Key words: task scheduling, cost-efficiency, cloud computing

CLC Number:

P751.1

YANG Yanan, LI Yiming, NIE Lihai, ZHANG Ning, ZHAO Laiping. Cost-Efficient Task Scheduling in Geo-distributed Datacenters[J]. Journal of Applied Sciences, 2019, 37(6): 859-874.

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Cost-Efficient Task Scheduling in Geo-distributed Datacenters

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