Online optimization for scheduling preemptable tasks on IaaS cloud systems

Authors:
Jiayin Li;Meikang Qiu;Zhong Ming;Gang Quan;Xiao Qin;Zonghua Gu
Affiliations:
Department of Elec. and Comp. Engr., University of Kentucky, Lexington, KY 40506, USA;Department of Elec. and Comp. Engr., University of Kentucky, Lexington, KY 40506, USA;College of Computer Science and Software, Shenzhen University, Shenzhen 518060, China;College of Engr. and Comp., Florida International University, Miami, FL 33174, USA;Department of Comp. Sci. and Software Engr., Auburn University, Auburn, AL 36849, USA;College of Computer Science, Zhejiang University, Hangzhou 310027, China
Venue:
Journal of Parallel and Distributed Computing
Year:
2012

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Abstract

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.