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

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Listed:
  • Shanchen Pang
  • Weiguang Zhang
  • Tongmao Ma
  • Qian Gao

Abstract

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.

Suggested Citation

  • Shanchen Pang & Weiguang Zhang & Tongmao Ma & Qian Gao, 2017. "Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, December.
    • Handle: RePEc:hin:jnlmpe:4810514
    DOI: 10.1155/2017/4810514
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    Cited by:

    1. Sudipto Mondal & Fashat Bin Faruk & Dibosh Rajbongshi & Mohammad Masum Khondhoker Efaz & Md. Motaharul Islam, 2023. "GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction," Sustainability, MDPI, vol. 15(21), pages 1-28, October.

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