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    An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity.

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    An Approach to Optimise Resource ...
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    Authors
    Panneerselvam, John cc
    Liu, Lu cc
    Antonopoulos, Nikolaos
    Affiliation
    University of Derby
    Issue Date
    2018-01-16
    
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    Abstract
    Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes increased complexities in achieving an energy efficient management of the Cloud jobs. The phenomenon of a few proportions of tasks characterising increased resource intensity within a given job usually lead the providers to over-provision all the encompassed tasks, resulting in majority of the tasks incurring an increased proportions of resource idleness. To this end, this paper proposes a novel analytics framework which integrates a resource estimation module to estimate the resource requirements of tasks a priori, a straggler classification module to classify tasks based on their resource intensity, and a resource optimisation module to optimise the level of resource provision depending on the task nature and various runtime factors. Performance evaluations conducted both theoretically and through practical experiments prove that the proposed methodology performs better than the compared statistical resource estimation methods and existing models of straggler mitigation, and further demonstrate the effectiveness of the proposed methodology in achieving energy conservation by postulating appropriate level of resource provisioning for task execution.
    Citation
    Panneerselvam, J. et al (2018) 'An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity.', IEEE Transactions on Emerging Topics in Computing, DOI: 10.1109/TETC.2018.2794328
    Publisher
    IEEE
    Journal
    IEEE Transactions on Emerging Topics in Computing
    URI
    http://hdl.handle.net/10545/622063
    DOI
    10.1109/TETC.2018.2794328
    Additional Links
    http://ieeexplore.ieee.org/document/8259477/
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1109/TETC.2018.2794328
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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