Online optimization for scheduling preemptable tasks on IaaS cloud systems

被引：274

作者：

Li, Jiayin ^{[2
]}

Qiu, Meikang ^{[2
]}

Ming, Zhong ^{[1
]}

Quan, Gang ^{[3
]}

Qin, Xiao ^{[4
]}

Gu, Zonghua ^{[5
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software, Shenzhen 518060, Peoples R China

[2] Univ Kentucky, Dept Elec & Comp Engr, Lexington, KY 40506 USA

[3] Florida Int Univ, Coll Engr & Comp, Miami, FL 33174 USA

[4] Auburn Univ, Dept Comp Sci & Software Engr, Auburn, AL 36849 USA

[5] Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China

来源：

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING | 2012年 / 72卷 / 05期

基金：

美国国家科学基金会;

关键词：

Cloud computing; Online scheduling; Feedback; Preemptable scheduling; ALGORITHMS; TIME;

D O I：

10.1016/j.jpdc.2012.02.002

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

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. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：666 / 677

页数：12

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