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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 4810514, 10 pages
Research Article

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

College of Computer & Communication Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China

Correspondence should be addressed to Shanchen Pang

Received 12 July 2017; Revised 25 November 2017; Accepted 4 December 2017; Published 20 December 2017

Academic Editor: Anna Vila

Copyright © 2017 Shanchen Pang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.