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A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments

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Abstract

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms.

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Authors and Affiliations

  1. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Mozhdeh Tanha & Mirsaeid Hosseini Shirvani

  2. Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Amir Masoud Rahmani

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  1. Mozhdeh Tanha
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  2. Mirsaeid Hosseini Shirvani
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Tanha, M., Hosseini Shirvani, M. & Rahmani, A.M. A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments. Neural Comput & Applic 33, 16951–16984 (2021). https://doi.org/10.1007/s00521-021-06289-9

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