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Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment

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Abstract

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk’s position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively.

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  1. Department of Computer Science, Shahid Bahonar University of Kerman, Box No. 76135-133, Kerman, Iran

    R. Ghafari & N. Mansouri

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  1. R. Ghafari
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RG: Programming, software development, Ideas NM: Development or design of methodology; creation of models, testing of existing code components, Writing- original draft preparation, Investigation

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Correspondence to N. Mansouri.

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Appendices

Appendices

1.1 Appendix 1: Chaotic maps details

See Table 13 .

Table 13 The description of chaotic maps [63]
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1.2 Appendix 2: Summary of compared meta-heuristic algorithms

See Table 14 .

Table 14 Summary information about the compared optimization algorithms
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1.3 Appendix 3: Description of benchmark functions

See Table 15 .

Table 15 Benchmark functions [36]
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1.4 Appendix 4: Results of DCOHHOTS compared to other algorithms

See Tables 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 .

Table 16 Makespan comparison (scenario 1)
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Table 17 Energy consumption comparison (scenario 1)
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Table 18 Resource utilization comparison (scenario 1)
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Table 19 Execution cost comparison (scenario 1)
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Table 20 Makespan comparison (scenario 2)
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Table 21 Energy consumption comparison (scenario 2)
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Table 22 Resource utilization comparison (scenario 2)
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Table 23 Execution cost comparison (scenario 2)
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Ghafari, R., Mansouri, N. Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment. Cluster Comput 27, 1421–1469 (2024). https://doi.org/10.1007/s10586-023-04021-x

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