Abstract
In this paper, for the problem of long task scheduling time and unbalanced system load in the task scheduling of cloud workflow. To minimize the task scheduling time and optimize load balancing as the scheduling goal, a Markov decision process model conforming to the cloud workflow environment is established. Based on this, a multi-objective optimization cloud workflow scheduling algorithm based on reinforcement learning is proposed. The algorithm combines Q_Learning features, adding a function with a weighted fitness value function in the Q_Learning reward function so that it can apply multi-objective optimization. The set of scheduling schemes is a Pareto optimal solution set, which can select the optimal scheduling scheme according to the user’s preference. Compared with other methods, this algorithm can reduce the execution time and optimize the system load. And this paper uses the real cloud workflow data to carry out the simulation experiment, and carries on the experiment through the simulation platform WorkflowSim. The result proves the effectiveness of this algorithm.
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Acknowledgement
Fund Project: National Natural Science Foundation of China (61772145, 61672174, 61272382), Guangdong Province Science and Technology Plan Project (2015B020233019, 2014A020208139).
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Jiahao, W., Zhiping, P., Delong, C., Qirui, L., Jieguang, H. (2018). A Multi-object Optimization Cloud Workflow Scheduling Algorithm Based on Reinforcement Learning. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_64
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