ADAPTIVE RESOURCE ALLOCATION FOR PREEMPTABLE JOBS IN CLOUD SYSTEMS PDF

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Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Adaptive resource allocation for preemptable jobs in cloud systems Abstract: In cloud computing, computational resources are provided to remote users in the form of leases.

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 tasks execution order.

Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.

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Adaptive Resource Allocation for Preemptable Jobs in Cloud Systems

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