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Research Article Open Access
In this paper, we consider a wireless cloud computing system in which the wireless service provider operates a data center and provides cloud services to its subscribers at dynamic prices. We propose a joint optimization of scheduling and pricing decisions for delay-tolerant batch services to maximize the service provider’s long-term profit. Unlike the existing research on jointly scheduling and pricing that focuses on static or asymptotic analysis, we focus on a dynamic setting and develop a provably efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which, without the necessity of predicting the future information, can be applied to an arbitrarily random environment that may follow an arbitrary trajectory over time. We prove that, compared to the optimal offline algorithm with future information, Dyn-SP produces a close-to-optimal average profit while bounding the job queue length in the data center. We perform a trace-based simulation study to validate Dyn-SP. Our results also indicate that, compared to the existing algorithms which neglect demand-side management, cooling system energy consumption, and/or the queue length information, Dyn-SP achieves a higher average profit while incurring (almost) the same average queueing delay.
Cloud computing, Pricing, Profit maximization, Scheduling, Stochastic optimization., Scientific Computing