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Optimized task scheduling in cloud computing using improved multi-verse optimizer

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Abstract

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach’s capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks.

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Data is available from the authors upon reasonable request.

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Funding

This study was financially supported via a funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project Number (TURSP-2020/300), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Mohammed Otair, Areej Alhmoud & Laith Abualigah

  2. School of Information Engineering, Sanming University, Sanming, 365004, China

    Heming Jia

  3. Department of Management Information System, College of Business Administration, Taif University, Taif, 21944, Saudi Arabia

    Maryam Altalhi

  4. Deanship of E-Learning and Distance Education, Umm Al-Qura University, Makkah, 21955, Saudi Arabia

    Ahmad MohdAziz Hussein

  5. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

Authors
  1. Mohammed Otair
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  2. Areej Alhmoud
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  3. Heming Jia
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  4. Maryam Altalhi
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  5. Ahmad MohdAziz Hussein
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  6. Laith Abualigah
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Correspondence to Laith Abualigah.

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Otair, M., Alhmoud, A., Jia, H. et al. Optimized task scheduling in cloud computing using improved multi-verse optimizer. Cluster Comput 25, 4221–4232 (2022). https://doi.org/10.1007/s10586-022-03650-y

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