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2016 IEEE 9th International Conference on Cloud Computing (2016)
San Francisco, California, USA
June 27, 2016 to July 2, 2016
ISSN: 2159-6190
ISBN: 978-1-5090-2619-7
pp: 849-854
Computational clouds have evolved to go beyond cost-effective on-demand hosting of IT resources and elasticity. The added features now include the ability to offer entire IT systems as a service that can quickly adapt to changing business environments. The new trend introduces challenges in datacenter resource management, including scalability, system-orientation, and optimization supporting both datacenter efficiency and customer system agility and performance. The conventional approach of resource management adopts a flat fine-grained model that results in problem formulations of enormous sizes, it also has the drawback of being less flexible in meeting customers' need. In this paper, we introduce a pack-centric approach to datacenter resource management by abstracting a system as a pack of resources and considering the mapping of these packs onto physical datacenter resource groups, called swads. The assignments of packs to swads are formulated as mixed integer programming problems. Scalability is achieved through a hierarchical decomposition method and parallel solvers. The new datacenter resource management framework is illustrated with a concrete resource placement problem. Numerical experiments show the scalability of the hierarchical decomposition method and the benefits of the overall framework.
Resource management, Cloud computing, Scalability, Linear programming, Servers, Computers

Y. Wang, Y. Xia, S. Chen, M. Tsugawa and J. A. Fortes, "Demonstrating Scalability and Efficiency of Pack-centric Resource Management for Cloud," 2016 IEEE 9th International Conference on Cloud Computing(CLOUD), San Francisco, California, USA, 2016, pp. 849-854.
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