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A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

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Abstract

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

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Notes

  1. http://www.acpi.info/.

  2. http://aws.amazon.com/ec2/instance-types/.

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Authors and Affiliations

  1. North Dakota State University, Fargo, USA

    Abdul Hameed & Samee U. Khan

  2. Australian National University, Canberra, Australia

    Alireza Khoshkbarforoushha

  3. CSIRO, Canberra, Australia

    Rajiv Ranjan & Prem Prakash Jayaraman

  4. Cracow University of Technology, Kraków, Poland

    Joanna Kolodziej

  5. Argonne National Laboratory, Lemont, USA

    Pavan Balaji

  6. University of the District of Columbia, Washington, USA

    Sherali Zeadally

  7. Qatar University, Doha, Qatar

    Qutaibah Marwan Malluhi

  8. Chinese Academy of Sciences, Beijing, China

    Nikos Tziritas

  9. Pacific Northwest National Laboratory, Richland, USA

    Abhinav Vishnu

  10. University of Sydney, Sydney, Australia

    Albert Zomaya

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  1. Abdul Hameed
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  2. Alireza Khoshkbarforoushha
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Correspondence to Rajiv Ranjan.

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Hameed, A., Khoshkbarforoushha, A., Ranjan, R. et al. A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98, 751–774 (2016). https://doi.org/10.1007/s00607-014-0407-8

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