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

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    Date
    2014-06
    Author
    Hameed, Abdul
    Khoshkbarforoushha, Alireza
    Ranjan, Rajiv
    Jayaraman, Prem Prakash
    Kolodziej, Joanna
    Balaji, Pavan
    Zeadally, Sherali
    Malluhi, Qutaibah Marwan
    Tziritas, Nikos
    Vishnu, Abhinav
    Khan, Samee U.
    Zomaya, Albert
    ...show more authors ...show less authors
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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.
    DOI/handle
    http://dx.doi.org/10.1007/s00607-014-0407-8
    http://hdl.handle.net/10576/4196
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    • Computer Science & Engineering [‎2428‎ items ]

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