Abstract
To the problem of scheduling multiple DAG workflow applications with multiple priorities submitted at different times in cloud computing environment, a novel workflow scheduling algorithm based on reinforcement learning is proposed in this paper. In the workflow scheduling scheme, the number of VMs in resources pool is defined as state space; the runtime of user task is defined as immediate reward, and then interactive with cloud computing environment to obtain the optimization policy. We use real cloud workflow to test the proposed scheme. Experiment results show the proposed scheme not only can solve the fairness of scheduling multiple DAGs with the same priority level submitted at different times, but also can ensure that the execution of the DAGs with higher priorities cannot be influenced by the DAGs with lower priorities. More importantly, the proposed scheme can reasonably schedule multiple DAGs with multiple priorities and improve utilization rate of resources better.
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Acknowledgments
The work presented in this paper was supported by National Natural Science Foundation of China (No. 61272382, 61402183). Key project of Guangdong Province in the research center of cloud robot (petrochemical) Engineering Technology (No. 650007). Guangdong Provincial Science and Technology Program (No. 2014A020208139); Distinguished Young Talents in Higher Education of Guangdong (No. 2013LYM-0057). 2014 Guangdong Provincial Technological Innovation Program (No. 650019). Wende Ke is corresponding author.
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Cui, D., Ke, W., Peng, Z., Zuo, J. (2016). Multiple DAGs Workflow Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_31
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DOI: https://doi.org/10.1007/978-981-10-0356-1_31
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