Cost‐minimizing online algorithm for internet green data centers on multi‐source energy

• Published in: Concurrency and computation Vol. 31; no. 21
• Main Authors: He, Huaiwen, Shen, Hong, Liang, Dieyan
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.11.2019
• Subjects: Algorithms; Alternative energy sources; Carbon; Computer centers; Computer simulation; Control algorithms; Control theory; Cost engineering; Data centers; Electricity pricing; Emission analysis; Energy conservation; Energy consumption; Energy costs; Energy resources; Environmental impact; geo‐distributed cloud center; green‐aware; Integer programming; Linear programming; Lyapunov optimization; Mixed integer; Optimization; Optimization techniques; Randomness; renewable energy; Renewable energy sources; Renewable resources; Workload; Workloads
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5044

Summary Huge energy consumption of large‐scale cloud data centers damages environments with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy. However, the intermittent availability of renewable energy sources makes it quite challenging to cooperate with dynamically arriving workload. Meanwhile, the different natures (eg, price and carbon emission) of multiple energy sources also bring more challenges to achieve an optimal trade‐off among carbon emission, power cost, and service level agreement (SLA). In this paper, we study the problem of reducing the long‐term energy cost for geo‐distributed cloud centers, where multiple sources of renewable energy are considered and SLA requirement and carbon budget are satisfied. To tackle the randomness of workload arrival, varying electricity price, and intermittent supply of renewable energy, we first formulate the cost minimization problem as a constraint stochastic optimization problem. Second, based on Lyapunov optimization technique, we propose an online control algorithm to solve it and provide the rigorous theory analysis to demonstrate its performance. By converting the long‐term optimization problem to a mixed integer linear programming problem in each time slot, we analyze its inherent structure and propose an efficient algorithm to solve it based on Brenner's method. Our proposed algorithm makes online decisions rely only on the current system state and achieve [O(1V),O(V)] cost emission trade‐off. Finally, the effectiveness of our algorithm is evaluated by extensive simulations based on real‐world data traces.