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Issue No.08 - August (2011 vol.44)
pp: 55-62
Sungkap Yeo , Georgia Inst. of Technol., Atlanta, GA, USA
Cloud computing has emerged as a highly cost-effective computation paradigm for IT enterprise applications, scientific computing, and personal data management. Because cloud services are provided by machines of various capabilities, performance, power, and thermal characteristics, it is challenging for providers to understand their cost effectiveness when deploying their systems. This article analyzes a parallelizable task in a heterogeneous cloud infrastructure with mathematical models to evaluate the energy and performance trade-off. As the authors show, to achieve the optimal performance per utility, the slowest node's response time should be no more than three times that of the fastest node. The theoretical analysis presented can be used to guide allocation, deployment, and upgrades of computing nodes for optimizing utility effectiveness in cloud computing services.
information technology, cloud computing, thermal characteristics, mathematical modeling, heterogeneous cloud computing environment, IT enterprise application, scientific computing, personal data management, Cloud computing, Peer to peer computing, Program processors, Time factors, Computational modeling, Virtual machining, Cost benefit analysis, Mathematical modeling, Cloud computing
Sungkap Yeo, "Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment", Computer, vol.44, no. 8, pp. 55-62, August 2011, doi:10.1109/MC.2011.96
1. M. Palankar et al., "Amazon S3 for Science Grids: A Viable Solution?" Proc. 2008 Int'l Workshop Data-Aware Distributed Computing, ACM Press, 2008, pp. 55-64.
2. L.A. Barroso, "The Price of Performance," ACM Queue, vol. 3, no. 7, 2005, pp. 48-53.
3. S. Ghiasi, T. Keller, and F. Rawson, "Scheduling for Heterogeneous Processors in Server Systems," Proc. 2nd Conf. Computing Frontiers, ACM Press, 2005, pp. 199-210.
4. R. Nathuji, C. Isci, and E. Gorbatov, "Exploiting Platform Heterogeneity for Power-Efficient Datacenters," Proc. 4th Int'l Conf. Autonomic Computing (ICAC 07), IEEE CS Press, 2007, pp. 5-14.
5. PassMark Software, "CPU Benchmarks;"
6. S. Pelley et al., "Power Routing: Dynamic Power Provisioning in the Datacenter," Proc. 15th Int'l Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS 10), ACM Press, 2010, pp. 231-242.
7. D. Meisner, B.T. Gold, and T.F. Wenisch, "PowerNap: Eliminating Server Idle Power," Proc. 14th Int'l Conf. Architectural Support for Programming Languages and Operating Systems, (ASPLOS 09), ACM Press, 2009, pp. 205-216.
8. R. Gonzalez and M. Horowitz, "Energy Dissipation in General Purpose Processors," IEEE J. Solid-State Circuits, vol. 31, no. 9, 1996, pp. 1277-1284.
9. J.A. Rice, Mathematical Statistics and Data Analysis, Duxbury Press, 2007.
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