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PCP–ACO: a hybrid deadline-constrained workflow scheduling algorithm for cloud environment

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Abstract

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively.

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Data availibility

All materials including source codes, scientific workflows used for experiments, and experiments results are available at https://github.com/PeymanShobeiri/PCP--ACO.

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Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. Department of Computer Science, Islamic Azad University, Mashhad, Khorasan Razavi, Iran

    Peyman Shobeiri

  2. Department of Computer Science, University of Alberta, Edmonton, AB, Canada

    Mehdi Akbarian Rastaghi

  3. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Khorasan Razavi, Iran

    Saeid Abrishami

  4. Department of Information Systems Security, University of Concordia, Montreal, QC, Canada

    Behnam Shobiri

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  1. Peyman Shobeiri
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  2. Mehdi Akbarian Rastaghi
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  3. Saeid Abrishami
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  4. Behnam Shobiri
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Contributions

SA and MAR contributed in conceptualization and proposing the algorithm. PS and MAR contributed in implementation and preparing results. SA, PS and BS wrote the final manuscripts. All Authors reviewed the manuscript.

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Correspondence to Saeid Abrishami.

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Shobeiri, P., Akbarian Rastaghi, M., Abrishami, S. et al. PCP–ACO: a hybrid deadline-constrained workflow scheduling algorithm for cloud environment. J Supercomput 80, 7750–7780 (2024). https://doi.org/10.1007/s11227-023-05753-8

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