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An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Author

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  • Xiao-Fang Liu

    (Department of Computer Science, Sun Yat-sen University, Guangzhou 510006, China
    School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

  • Zhi-Hui Zhan

    (School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

  • Jun Zhang

    (School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

Abstract

Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

Suggested Citation

  • Xiao-Fang Liu & Zhi-Hui Zhan & Jun Zhang, 2017. "An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing," Energies, MDPI, vol. 10(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:609-:d:97321
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    References listed on IDEAS

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    1. Gustavo Callou & João Ferreira & Paulo Maciel & Dietmar Tutsch & Rafael Souza, 2014. "An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and Cost," Energies, MDPI, vol. 7(1), pages 1-40, January.
    2. Chin-Chi Cheng & Dasheng Lee & Ching Hung Wang & Shu Fen Lin & Hung-Peng Chang & Shang-Te Fang, 2015. "The Development of Cloud Energy Management," Energies, MDPI, vol. 8(5), pages 1-21, May.
    3. Xiaolong Cui & Bryan Mills & Taieb Znati & Rami Melhem, 2014. "Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing," Energies, MDPI, vol. 7(8), pages 1-26, August.
    4. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    5. Luca Chiaraviglio & Antonio Cianfrani & Marco Listanti & William Liu & Marco Polverini, 2016. "Lifetime-Aware Cloud Data Centers: Models and Performance Evaluation," Energies, MDPI, vol. 9(6), pages 1-17, June.
    6. Setzer, Thomas & Bichler, Martin, 2013. "Using matrix approximation for high-dimensional discrete optimization problems: Server consolidation based on cyclic time-series data," European Journal of Operational Research, Elsevier, vol. 227(1), pages 62-75.
    7. Jorge Lanza & Luis Sánchez & Verónica Gutiérrez & José Antonio Galache & Juan Ramón Santana & Pablo Sotres & Luis Muñoz, 2016. "Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case," Energies, MDPI, vol. 9(9), pages 1-14, September.
    8. Qunli Wu & Chenyang Peng, 2016. "A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction," Energies, MDPI, vol. 9(8), pages 1-20, July.
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    Cited by:

    1. Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. S. H. Alsamhi & Ou Ma & Mohd. Samar Ansari & Qingliang Meng, 2019. "Greening internet of things for greener and smarter cities: a survey and future prospects," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 609-632, December.

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