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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
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.
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
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
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