IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i5p609-d97321.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/5/609/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/5/609/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lei Chen & Xiude Tu & Hongkun Chen & Jun Yang & Yayi Wu & Xin Shu & Li Ren, 2016. "Technical Evaluation of Superconducting Fault Current Limiters Used in a Micro-Grid by Considering the Fault Characteristics of Distributed Generation, Energy Storage and Power Loads," Energies, MDPI, vol. 9(10), pages 1-21, September.
    2. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    3. Droutsa, Kalliopi G. & Kontoyiannidis, Simon & Dascalaki, Elena G. & Balaras, Constantinos A., 2016. "Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data," Energy, Elsevier, vol. 98(C), pages 284-295.
    4. 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.
    5. Pio Alessandro Lombardi & Kranthi Ranadheer Moreddy & André Naumann & Przemyslaw Komarnicki & Carmine Rodio & Sergio Bruno, 2019. "Data Centers as Active Multi-Energy Systems for Power Grid Decarbonization: A Technical and Economic Analysis," Energies, MDPI, vol. 12(21), pages 1-14, November.
    6. English, J. & Niet, T. & Lyseng, B. & Palmer-Wilson, K. & Keller, V. & Moazzen, I. & Pitt, L. & Wild, P. & Rowe, A., 2017. "Impact of electrical intertie capacity on carbon policy effectiveness," Energy Policy, Elsevier, vol. 101(C), pages 571-581.
    7. Ahmed WA Hammad & Ali Akbarnezhad & Assed Haddad & Elaine Garrido Vazquez, 2019. "Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities," Energies, MDPI, vol. 12(7), pages 1-23, April.
    8. Weide Li & Demeng Kong & Jinran Wu, 2017. "A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(5), pages 1-16, May.
    9. Jose Alejandro Cano & Abraham Londoño-Pineda & Maria Fanny Castro & Hugo Bécquer Paz & Carolina Rodas & Tatiana Arias, 2022. "A Bibliometric Analysis and Systematic Review on E-Marketplaces, Open Innovation, and Sustainability," Sustainability, MDPI, vol. 14(9), pages 1-42, May.
    10. Ming-Wei Li & Jing Geng & Wei-Chiang Hong & Yang Zhang, 2018. "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting," Energies, MDPI, vol. 11(9), pages 1, August.
    11. Barbour, Edward & Wilson, I.A. Grant & Radcliffe, Jonathan & Ding, Yulong & Li, Yongliang, 2016. "A review of pumped hydro energy storage development in significant international electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 421-432.
    12. Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
    13. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    14. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    15. Cheng-Wen Lee & Bing-Yi Lin, 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting," Energies, MDPI, vol. 10(11), pages 1-18, November.
    16. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.
    17. Bennaceur, Walid Mokhtar & Kloul, Leïla, 2020. "Formal models for safety and performance analysis of a data center system," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    18. Maria Avgerinou & Paolo Bertoldi & Luca Castellazzi, 2017. "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, MDPI, vol. 10(10), pages 1-18, September.
    19. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    20. Joao Ferreira & Gustavo Callou & Dietmar Tutsch & Paulo Maciel, 2018. "PLDAD—An Algorihm to Reduce Data Center Energy Consumption," Energies, MDPI, vol. 11(10), pages 1-24, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:609-:d:97321. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.