期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 13, 期 10, 页码 2283-2292出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2013.57
关键词
Cloud computing; pricing; profit maximization; scheduling; stochastic optimization
资金
- US National Science Foundation [0830556, 1218136]
- Direct For Computer & Info Scie & Enginr [1218136] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [1218136] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0830556] Funding Source: National Science Foundation
In this paper, we consider a wireless cloud computing system in which the 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. In particular, we show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. 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.
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