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Combinatorial meta-heuristics approaches for DVFS-enabled green clouds

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Abstract

Many scientific applications used in decision support systems successfully make use of nature-based resourceful techniques. The advancements being made in successfully mimicking nature are laying the path for designing energy-efficient clouds. Two meta-heuristic techniques including ant colony optimization and particle swarm optimization, in combination with Bayesian and fuzzy approach, are proposed to be used in this research for designing an energy-efficient cloud system, which adopts the dynamic voltage and frequency scaling (DVFS) method. As DVFS is increasingly becoming an industry standard owing to its incorporation into the CPU hardware, appropriate software-oriented approaches are essential to calibrate the current methodologies. Our research aims at minimizing the accomplishment time and cost, enhancing user satisfaction, and lowering energy consumption. We generated results that excelled the current performance factors on multiple counts.

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Authors and Affiliations

  1. Department of CSE, SCAD College of Engineering and Technology, Cheranmahadevi, Tamil Nadu, India

    Lourdes Mary Amulu

  2. Department of CSE, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

    Ravi Ramraj

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  1. Lourdes Mary Amulu
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  2. Ravi Ramraj
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Correspondence to Lourdes Mary Amulu.

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Amulu, L.M., Ramraj, R. Combinatorial meta-heuristics approaches for DVFS-enabled green clouds. J Supercomput 76, 5825–5834 (2020). https://doi.org/10.1007/s11227-019-02997-1

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