Joint design of Dynamic Scheduling and Pricing in wireless cloud computing

In this paper, we consider a wireless cloud computing system in which a profit-maximizing wireless service provider provides cloud computing services to its subscribers. In particular, we focus on batch services, which, due to their non-urgent nature, allow more scheduling flexibility than their interactive counterparts. Unlike the existing research that studied separately demand-side management and energy cost saving techniques (both of which are critical to profit maximization), we propose a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which proactively adapts the service demand to workload scheduling in the data center and opportunistically utilizes low electricity prices to process batch jobs for energy cost saving. Without the necessity of predicting future information as assumed by some prior works, Dyn-SP can be applied to an arbitrarily random environment in which the electricity price, available renewable energy supply, and wireless network capacities may evolve over time as arbitrary stochastic processes. It is proved that, compared to the optimal offline algorithm with future information, Dyn-SP can produce a close-to-optimal longterm profit while bounding the job queue length in the data center. We also show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Finally, we perform a simulation study to demonstrate the effectiveness of Dyn-SP.