Online optimization for scheduling preemptable tasks on IaaS cloud systems

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• 作者：Jiayin Li^a ; ^{jli6@engr.uky.edu}  ; [Author Vitae] ; Meikang Qiu^a ; ^{mqiu@engr.uky.edu} ; ^{qiumeikang@yahoo.com}  ; [Author Vitae] ; Zhong Ming^b ; ^{mingz@szu.edu.cn}  ; [Author Vitae] ; Gang Quan^c ; ^{Gang.Quan@fiu.edu}  ; [Author Vitae] ; Xiao Qin^d ; ^{xqin@auburn.edu}  ; [Author Vitae] ; Zonghua Gu^e ; ^{zgu@zju.edu.cn}  ; [Author Vitae]
• 关键词：Cloud computing ; Online scheduling ; Feedback ; Preemptable scheduling
• 刊名：Journal of Parallel and Distributed Computing
• 出版年：2012
• 期刊代码：188_07437315
• 类别：et
• 出版时间：May, 2012
• 卷：72
• 期：5
• 页码：666-677
• 文件大小：1352 K

摘要

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.