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A two-stage scheduling method for deadline-constrained task in cloud computing

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Abstract

In a cloud environment, reducing energy consumption while ensuring diverse quality of service (QoS) guarantees is challenging for task schedulers. Specifically, the energy-efficient scheduling for real-time tasks is more complicated because such tasks have strict time constraints. In this paper, we propose a two-stage scheduling method for deadline-constrained tasks. In the first stage, Enhanced Ant Colony Optimization (EACO) is a global scheduler that allocates incoming cloud tasks to suitable virtual machines (VMs). It can minimize makespan and energy consumption while guaranteeing strict deadline constraints. In the second stage, the Modified Backfilling (MBF) algorithm reorders VM’s waiting queue to improve the task completion rate. We conduct two experiment series on synthetic and real trace datasets using the Cloudsim toolkit. Extensive experiments show that compared with other well-known task scheduling methods, our method can effectively reduce makespan by 25.28% and energy consumption by 23% on average. The task completion rate can be increased by 6.27%. The proposed method has a significant improvement compared with other well-known algorithms.

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Acknowledgements

This work was supported in part by the Guangdong Major Project of Basic and Applied Basic Research under Grant 2019B030302002, in part by the Science and Technology Major Project of Guangzhou under number 202007030006, in part by the Industrial Development Fund Project of Guangzhou under Project x2jsD8183470, in part by the Engineering and Technology Research Center of Guangdong Province for Logistics Supply Chain and Internet of Things under Grant GDDST[2016]176, and in part by the Hi-Tech Industrialization Entrepreneurial Team Project of Foshan Hi-Tech Zone under Grant FSHT[2020]88.

Funding

This work was supported in part by the Guangdong Major Project of Basic and Applied Basic Research under Grant 2019B030302002, in part by the Science and Technology Major Project of Guangzhou under number 202007030006, in part by the Industrial Development Fund Project of Guangzhou under Project x2jsD8183470, in part by the Engineering and Technology Research Center of Guangdong Province for Logistics Supply Chain and Internet of Things under Grant GDDST[2016]176, and in part by the Hi-Tech Industrialization Entrepreneurial Team Project of Foshan Hi-Tech Zone under Grant FSHT[2020]88.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Xiaojian He, Junmin Shen, Fagui Liu, Guoxiang Zhong & Jun Jiang

  2. Cyberspace Security Research Center, Peng Cheng Laboratory, ShenZhen, China

    Bin Wang

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  1. Xiaojian He
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  2. Junmin Shen
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  4. Bin Wang
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Contributions

All authors contributed to the study conception and methodology. [XH] and [FL] participate and guide the whole work. [JS] conducts the whole process of the experiment. The project comes from [FL]. [BW], [GZ], and [JJ] review and correctness the draft. The first draft of the manuscript was written by [Junmin Shen] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Fagui Liu or Bin Wang.

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He, X., Shen, J., Liu, F. et al. A two-stage scheduling method for deadline-constrained task in cloud computing. Cluster Comput 25, 3265–3281 (2022). https://doi.org/10.1007/s10586-022-03561-y

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