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Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
ISBN: 978-1-4673-2892-0
pp: 17-24
ABSTRACT
As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration.
INDEX TERMS
Servers, Resource management, Runtime, Optimization, Vectors, Cloud computing, Random access memory, migration, cloud computing, virtual machine, deployment optimization, runtime reconfiguration
CITATION
Wei Chen, Xiaoqiang Qiao, Jun Wei, Tao Huang, "A Profit-Aware Virtual Machine Deployment Optimization Framework for Cloud Platform Providers", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 17-24, doi:10.1109/CLOUD.2012.60
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