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Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm

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Abstract

Multi-cloud is the use of multiple cloud computing in a single heterogeneous architecture. Workflow scheduling in multi-cloud computing is an NP-Hard problem for which many heuristics and meta-heuristics are introduced. This paper first presents a hybrid multi-objective optimization algorithm denoted as HGSOA-GOA, which combines the Seagull Optimization Algorithm (SOA) and Grasshopper Optimization Algorithm (GOA). The HGSOA-GOA applies chaotic maps for producing random numbers and achieves a good trade-off between exploitation and exploration, leading to an improvement in the convergence rate. Then, HGSOA-GOA is applied for scientific workflow scheduling problems in multi-cloud computing environments by considering factors such as makespan, cost, energy, and throughput. In this algorithm, a solution from the Pareto front is selected using a knee-point method and then is applied for assigning the scientific workflows’ tasks in a multi-cloud environment. Extensive comparisons are conducted using the CloudSim and WorkflowSim tools and the results are compared to the SPEA2 algorithm. The achieved results exhibited that the HGSOA-GOA can outperform other algorithms in terms of metrics such as IGD, coverage ratio, and so on.

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Authors and Affiliations

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

    Ali Mohammadzadeh

  2. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Mohammad Masdari

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  1. Ali Mohammadzadeh
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  2. Mohammad Masdari
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Correspondence to Ali Mohammadzadeh.

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Mohammadzadeh, A., Masdari, M. Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm. J Ambient Intell Human Comput 14, 3509–3529 (2023). https://doi.org/10.1007/s12652-021-03482-5

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