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QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

  • Advances in Parallel and Distributed Computing for Neural Computing
  • Published:
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Abstract

Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q-learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank (ranku) value of HEFT as the immediate reward in the Q-learning framework. The agent can obtain better learning results to update the Q-table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q-learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time.

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Acknowledgements

This research was partially funded by the Program of National Natural Science Foundation of China (Grant No. 61502165, 61602170), the National Outstanding Youth Science Program of National Natural Science Foundation of China (Grant No. 61625202) and the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 17C0959).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, Changsha, 410012, Hunan, China

    Zhao Tong, Xiaomei Deng, Hongjian Chen, Jing Mei & Hong Liu

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  1. Zhao Tong
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  2. Xiaomei Deng
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  3. Hongjian Chen
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  5. Hong Liu
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Correspondence to Zhao Tong.

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Tong, Z., Deng, X., Chen, H. et al. QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment. Neural Comput & Applic 32, 5553–5570 (2020). https://doi.org/10.1007/s00521-019-04118-8

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