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Article

Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers

by 1,*, 2 and 3
1
Business School, Xuzhou University of Technology, Xuzhou 221018, China
2
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
3
School of Business, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Academic Editor: Khac Duc Do
Systems 2022, 10(2), 27; https://doi.org/10.3390/systems10020027
Received: 24 January 2022 / Revised: 26 February 2022 / Accepted: 28 February 2022 / Published: 2 March 2022
(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)
In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast setup process occurs when the server’s states are changed from sleep to work. The servers in Group 1 are faster and cheaper than those of Group 2 so that Group 1 has a higher service priority. Putting each server in Group 2 to sleep can reduce system costs and energy consumption, but it must bear setup costs and transfer costs. For such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, both of which determine the switch rule between the work and sleep states for each server in Group 2. To find the optimal asynchronous dynamic policy, we apply the sensitivity-based optimization to establish a block-structured policy-based Markov process and use a block-structured policy-based Poisson equation to compute the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that a bang–bang control is always optimal for this optimization problem. We hope that the methodology and results developed in this paper can shed light on the study of more general energy-efficient data centers. View Full-Text
Keywords: asynchronous dynamic policy; energy-efficient data center; Markov decision process; RG-factorization; sensitivity-based optimization asynchronous dynamic policy; energy-efficient data center; Markov decision process; RG-factorization; sensitivity-based optimization
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MDPI and ACS Style

Ma, J.-Y.; Li, Q.-L.; Xia, L. Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers. Systems 2022, 10, 27. https://doi.org/10.3390/systems10020027

AMA Style

Ma J-Y, Li Q-L, Xia L. Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers. Systems. 2022; 10(2):27. https://doi.org/10.3390/systems10020027

Chicago/Turabian Style

Ma, Jing-Yu, Quan-Lin Li, and Li Xia. 2022. "Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers" Systems 10, no. 2: 27. https://doi.org/10.3390/systems10020027

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