Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center
Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center
复制标题
云数据中心动态能源管理的蚁群优化算法
DOI:
10.1155/2017/4810514
复制
发表时间:
2017-12
影响因子:
--
通讯作者:
Gao Qian
中科院分区:
文献类型:
--
作者:
Pang Shanchen;Zhang Weiguang;Ma Tongmao;Gao Qian
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.
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