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Dynamic scheduling of independent tasks in cloud computing applying a new hybrid metaheuristic algorithm including Gabor filter, opposition-based learning, multi-verse optimizer, and multi-tracker optimization algorithms

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Abstract

The cloud runtime environment is dynamic; therefore, allocating tasks to computing resources might include various scenarios. Metaheuristic algorithms are usually used to choose appropriate scheduling scenarios; however, they suffer from premature convergence, trapping in local optima, and imbalance between the exploration and exploitation of search space. The multi-verse optimizer (MVO) algorithm also suffers from similar problems. In this research, both Gabor filter and opposition-based learning methods are applied in the MVO algorithm to present the new algorithm GOMVO. The multi-tracker optimization (MTO) is applied in the GOMVO to present the new MTO-GOMVO hybrid algorithm. Then the scheduling framework MTOA-GOMVO@DSF is presented that applies the MTO-GOMVO metaheuristic algorithms in cloud computing scheduling. In the sequel, at first, the GOMVO algorithm is benchmarked applying CEC2017 benchmark functions and compared with several baseline algorithms in terms of mean error. Second, MTOA-GOMVO is also evaluated against related baseline algorithms in terms of mean error. Finally, MTOA-GOMVO is also applied in cloud computing to schedule independent tasks to virtual machines to improve average execution time, response time, throughput, and SLA violations. Simulation results applying NASA-iPSC real dataset showed that MTOA-GOMVO outweighs the baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks.

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Notes

  1. Multi-Tracker Optimization Algorithm-Gabor OBL Multi-Verse Optimizer @ Dynamic Scheduling Framework (MTOA-GOMVO @ DSF).

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Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Ahmad Nekooei-Joghdani & Faramarz Safi-Esfahani

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Ahmad Nekooei-Joghdani & Faramarz Safi-Esfahani

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  1. Ahmad Nekooei-Joghdani
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  2. Faramarz Safi-Esfahani
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Correspondence to Faramarz Safi-Esfahani.

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Appendix

Appendix

In this section, two Appendixes 1 and 2, are presented. In “Appendix 1,” the results have presented the reason for selecting the proposed GOMVO metaheuristic algorithm compared to other algorithms such as BAT, PSO, WOA and MVO so that this algorithm has better results than other algorithms for optimization, as shown in the results of this section.

In “Appendix 2,” the first section presents the results that show which versions of the CMVO algorithm have the best results to compare our proposed algorithm with the results of the best version of this CMVO5; then, the Friedman test is used for more reliability.

Finally, in the second part of “Appendix 2,” the proposed MTOA-GOMVO algorithm is compared with other CMVO5, MVO Levy, GOMVO, MTOA, and MVO metaheuristic algorithms, and the results show that the MTOA-GOMVO algorithm has better scheduling than other algorithms.

1.1 Appendix 1: GOMVO algorithm Implementation results in MATLAB software

See Tables 13, 14, and 15.

Table 13 The comparison results of the GOMVO proposed algorithm with the baseline methods in terms of the performances average
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Table 14 The Freidman test comparison of the GOMVO with the baseline algorithms
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Table 15 The  comparison results of the GOMVO algorithms against BAT, PSO, WOA, and MVO algorithms
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1.2 Appendix 2: MTOA-GOMVO algorithm Implementation results in MATLAB software

See Tables 16, 17, 18, and 19.

Table 16 The  comparison results of different versions of the CMVO algorithm
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Table 17 The Freidman test results for different version of the CMVO algorithm
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Table 18 The comparison of the MTOA-GOMVO proposed algorithm with the baseline algorithms
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Table 19 The Freidman test comparison of the MTOA-GOMVO proposed algorithm with the baseline algorithms
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Nekooei-Joghdani, A., Safi-Esfahani, F. Dynamic scheduling of independent tasks in cloud computing applying a new hybrid metaheuristic algorithm including Gabor filter, opposition-based learning, multi-verse optimizer, and multi-tracker optimization algorithms. J Supercomput 78, 1182–1243 (2022). https://doi.org/10.1007/s11227-021-03814-4

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