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A novel seq2seq-based prediction approach for workflow scheduling

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Abstract

Workflow scheduling problems have been widely studied in cloud computing and edge computing, which aim to exploit cloud-edge resources to execute workflow tasks considering several constraints and optimization goals. However, in the era of Internet of things, the load of each computing task and the amount of data transferred between computing tasks will fluctuate, which changes the original workflow and needs for a new scheduling plan correspondingly. Existing methods are difficult to quickly cope with these dynamic changes and there are few studies applying neural networks to solve problems in workflow scheduling. To bridge the gap, we propose an innovative supervised learning method which leverages function-fitting strategy of neural networks to link the workflow environment and its optimal scheduling plan. Specifically, our approach can be divided into two steps, the first one is to generate dataset and train a seq2seq-based prediction models. In this step, we develop an algorithm for generating a significant amount of workflow instances while ensuring dataset diversity based on complexity estimation. Then we apply GA, NSGA, NSGA-NN three different types GA-based optimization methods to search optimal solutions. Finally, we construct dataset which includes {workflow, environment configurations \(\rightarrow\) obtained optimal solution} and train a seq2seq-based model. The other part is real-time generation of scheduling plans based on trained seq2seq-based model. Simulation experiments have confirmed that our method is both effective and efficient, demonstrating its ability to adapt to changes in the execution environment, workflow task load, and task data transmission, and effectively schedule tasks in real-time. The simulation results show that the seq2seq-based prediction method can approach 90% of the optimal scheme.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work is supported by the International Cooperation and Exchange Program of National Natural Science Foundation of China (Grant no. 62061136006) and National Natural Science Foundation of China (Grant no. 61832004).

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Zhongguo Yang, Han Li & Weilong Ding

  2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, Beijing, 100144, China

    Zhongguo Yang, Han Li & Weilong Ding

  3. Department of Informatics, Beijing University of Technology, Beijing, 100124, China

    Mingzhu Zhang

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  1. Zhongguo Yang
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  2. Mingzhu Zhang
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  3. Han Li
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  4. Weilong Ding
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Contributions

ZY: Conceptualization, Methodology; MZ: Data curation, Software, Writing—Original draft preparation; HL: Validation, Visualization; WD: Validation, Writing—Reviewing and Editing; All authors reviewed the manuscript.

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Correspondence to Zhongguo Yang.

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Yang, Z., Zhang, M., Li, H. et al. A novel seq2seq-based prediction approach for workflow scheduling. Cluster Comput 27, 1897–1910 (2024). https://doi.org/10.1007/s10586-023-04061-3

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  • DOI: https://doi.org/10.1007/s10586-023-04061-3

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