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A Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing

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  • Rajeshwari Sissodia

    (Hemwati Nandan Bahuguna Garhwal University, India)

  • ManMohan Singh Rauthan

    (Hemwati Nandan Bahuguna Garhwal University, India)

  • Varun Barthwal

    (Hemwati Nandan Bahuguna Garhwal University, India)

Abstract

Cloud data centers (CDC) have become an increasingly critical issue because of their large-scale deployment, which has resulted in increased energy consumption (EC) and SLA. The SLA and EC can be greatly reduced by using an efficient virtual machine consolidation (VMC) approach. This study presents a multi-objective adaptive upper threshold (UTh) technique for identifying overloaded hosts. The dynamic virtual machine consolidation (DVMC) is then obtained by combining a modified overloaded host detection technique with a different VM selection method (i.e., minimum migration time (Mmt) and minimum utilization (Mu)). The simulation results indicate that the modified Interquartile range (Iqr) overloaded host detection algorithm outperforms the existing overloaded host detection algorithms (i.e., InterQuartile range (Iqr), local regression (Lr), and dynamic voltage frequency scale (DVFS) algorithms) in terms of EC, SLA, and the number of virtual machine (VM) migrations.

Suggested Citation

  • Rajeshwari Sissodia & ManMohan Singh Rauthan & Varun Barthwal, 2022. "A Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(1), pages 1-14, January.
    • Handle: RePEc:igg:jcac00:v:12:y:2022:i:1:p:1-14
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