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Efficient Prediction of Makespan Matrix Workflow Scheduling Algorithm for Heterogeneous Cloud Environments

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Abstract

Leveraging a cloud computing environment for executing workflow applications offers high flexibility and strong scalability, thereby significantly improving resource utilization. Current scholarly discussions heavily focus on effectively reducing the scheduling length (makespan) of parallel task sets and improving the efficiency of large workflow applications in cloud computing environments. Effectively managing task dependencies and execution sequences plays a crucial role in designing efficient workflow scheduling algorithms. This study forwards a high-efficiency workflow scheduling algorithm based on predict makespan matrix (PMMS) for heterogeneous cloud computing environments. First, PMMS calculates the priority of each task based on the predict makespan (PM) matrix and obtains the task scheduling list. Second, the optimistic scheduling length (OSL) value of each task is calculated based on the PM matrix and the earliest finish time. Third, the best virtual machine is selected for each task according to the minimum OSL value. A large number of substantial experiments show that the scheduling length of workflow for PMMS, compared with state-of-the-art HEFT, PEFT, and PPTS algorithms, is reduced by 6.84%–15.17%, 5.47%–11.39%, and 4.74%–17.27%, respectively. This hinges on the premise of ensuring priority constraints and not increasing the time complexity.

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Data Availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was partially funded by the National Key R &D Program of China (Grant No. 2018YFB1003401), the Natural Science Foundation of Hunan Province (Grant Nos. 2023JJ50204, 2022JJ50002, 2021JJ50049), the Scientific Research Foundation of Hunan Provincial Education Department (Grant Nos. 23B0562, 22B0598) and the National Natural Science Foundation of China (Grant Nos. 61702178, 62072172).

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Authors and Affiliations

  1. School of Computer Science, Hunan University of Technology, Taishan, Zhuzhou, 412007, Hunan, China

    Longxin Zhang, Minghui Ai, Runti Tan & Xiaojun Deng

  2. School of Computer Science, Hunan First Normal University, Yuelu, Changsha, 410205, Hunan, China

    Junfeng Man

  3. Department of Computer Science, State Universityof New York, 100 Hawk Drive, New Paltz, 12561, NY, USA

    Keqin Li

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  1. Longxin Zhang
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  2. Minghui Ai
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Contributions

Longxin Zhang: Methodology, Data curation, Formal analysis, Software, Visualization, Writing - original draft, Writing - review & editing, Supervision. Minghui Ai: Conceptualization, Project administration, Writing - original draft. Runti Tan: Resources, Validation. Junfeng Man: Data curation, Formal analysis, Software. Xiaojun Deng: Data annotation, Investigation, Visualization. Keqin Li: Writing - review & editing, Supervision.

Corresponding authors

Correspondence to Longxin Zhang or Junfeng Man.

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The authors declare that they have no known conflict financial interests or personal relationships that could have appeared to influence the work reported in this study.

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Zhang, L., Ai, M., Tan, R. et al. Efficient Prediction of Makespan Matrix Workflow Scheduling Algorithm for Heterogeneous Cloud Environments. J Grid Computing 21, 75 (2023). https://doi.org/10.1007/s10723-023-09711-9

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