期刊
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
卷 72, 期 5, 页码 666-677出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2012.02.002
关键词
Cloud computing; Online scheduling; Feedback; Preemptable scheduling
资金
- University of Kentucky
- NSFC [61071061, 61170077, 61070002]
- SZ-HK [ZYB200907060012A]
- NSF [GD: 10351806001000000, CNS-0969013, CNS-0917021, CNS-1018108, CNS-0915762, CCF-08452578, CNS-0917137, CNS-0757778, CCF-0742187, CNS-0831502, CNS-0855251, OCI-0753305, DUE-0837341, DUE-0830831]
- SZ [JC200903120046A]
- Auburn Start Up grant
- Intel [2005-04-070]
- [MOE-INTEL-10-02]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0969013, 1018108, 0917137] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0845257] Funding Source: National Science Foundation
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.
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