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Issue No.06 - June (2013 vol.24)
pp: 1087-1096
Junwei Cao , Tsinghua University, Beijing
Kai Hwang , University of Southern California, Los Angeles
Keqin Li , State University of New York, New Paltz
Albert Y. Zomaya , University of Sydney, Sydney
ABSTRACT
As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.
INDEX TERMS
Servers, Cloud computing, Time factors, Computational modeling, Business, Random variables, Power demand, waiting time, Cloud computing, multiserver system, pricing model, profit, queuing model, response time, server configuration, service charge, service-level agreement
CITATION
Junwei Cao, Kai Hwang, Keqin Li, Albert Y. Zomaya, "Optimal Multiserver Configuration for Profit Maximization in Cloud Computing", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 6, pp. 1087-1096, June 2013, doi:10.1109/TPDS.2012.203
REFERENCES
[1] http://en.wikipedia.org/wikiCMOS, 2012.
[2] http://en.wikipedia.org/wikiService_level_agreement , 2012.
[3] M. Armbrust et al., "Above the Clouds: A Berkeley View of Cloud Computing," Technical Report No. UCB/EECS-2009-28, Feb. 2009.
[4] R. Buyya, D. Abramson, J. Giddy, and H. Stockinger, "Economic Models for Resource Management and Scheduling in Grid Computing," Concurrency and Computation: Practice and Experience, vol. 14, pp. 1507-1542, 2007.
[5] R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the Fifth Utility," Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
[6] A.P. Chandrakasan, S. Sheng, and R.W. Brodersen, "Low-Power CMOS Digital Design," IEEE J. Solid-State Circuits, vol. 27, no. 4, pp. 473-484, Apr. 1992.
[7] B.N. Chun and D.E. Culler, "User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers," Proc. Second IEEE/ACM Int'l Symp. Cluster Computing and the Grid, 2002.
[8] D. Durkee, "Why Cloud Computing Will Never be Free," Comm. ACM, vol. 53, no. 5, pp. 62-69, 2010.
[9] R. Ghosh, K.S. Trivedi, V.K. Naik, and D.S. Kim, "End-to-End Performability Analysis for Infrastructure-as-a-Service Cloud: An Interacting Stochastic Models Approach," Proc. 16th IEEE Pacific Rim Int'l Symp. Dependable Computing, pp. 125-132, 2010.
[10] K. Hwang, G.C. Fox, and J.J. Dongarra, Distributed and Cloud Computing. Morgan Kaufmann, 2012.
[11] "Enhanced Intel SpeedStep Technology for the Intel Pentium M Processor," White Paper, Intel, Mar. 2004.
[12] D.E. Irwin, L.E. Grit, and J.S. Chase, "Balancing Risk and Reward in a Market-Based Task Service," Proc. 13th IEEE Int'l Symp. High Performance Distributed Computing, pp. 160-169, 2004.
[13] H. Khazaei, J. Misic, and V.B. Misic, "Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems," IEEE Trans. Parallel and Distributed Systems, vol. 23, no. 5, pp. 936-943, May 2012.
[14] L. Kleinrock, Queueing Systems: Theory, vol. 1. John Wiley and Sons, 1975.
[15] Y.C. Lee, C. Wang, A.Y. Zomaya, and B.B. Zhou, "Profit-Driven Service Request Scheduling in Clouds," Proc. 10th IEEE/ACM Int'l Conf. Cluster, Cloud and Grid Computing, pp. 15-24, 2010.
[16] K. Li, "Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud Computing Environment," Proc. 25th IEEE Int'l Parallel and Distributed Processing Symp. Workshops, pp. 943-952, May 2011.
[17] K. Li, "Optimal Configuration of a Multicore Server Processor for Managing the Power and Performance Tradeoff," J. Supercomputing, vol. 61, no. 1, pp. 189-214, 2012.
[18] P. Mell and T. Grance, "The NIST Definition of Cloud Computing," Nat'l Inst. of Standards and Technology, http://csrc.nist. gov/groups/SNScloud-computing /, 2009.
[19] F.I. Popovici and J. Wilkes, "Profitable Services in an Uncertain World," Proc. ACM/IEEE Conf. Supercomputing, 2005.
[20] J. Sherwani, N. Ali, N. Lotia, Z. Hayat, and R. Buyya, "Libra: A Computational Economy-Based Job Scheduling System for Clusters," Software - Practice and Experience, vol. 34, pp. 573-590, 2004.
[21] C.S. Yeo and R. Buyya, "A Taxonomy of Market-Based Resource Management Systems for Utility-Driven Cluster Computing," Software - Practice and Experience, vol. 36, pp. 1381-1419, 2006.
[22] B. Zhai, D. Blaauw, D. Sylvester, and K. Flautner, "Theoretical and Practical Limits of Dynamic Voltage Scaling," Proc. 41st Design Automation Conf., pp. 868-873, 2004.
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