Workflow scheduling based on deep reinforcement learning in the cloud environment

As a convenient and economic computing model, cloud computing promotes the development of intelligence. Solving the workflow scheduling is a significant topic to promote the development of the cloud computing. In this work, an Actor-Critic architecture is utilized to solve this problem achieving the task executive time minimization under the task precedence constraint. It is similar to the list-based heuristic algorithm which includes the task prioritizing phase and task allocation phase. However, the results of the two phases interact with each other. In the task prioritizing phase, given a workflow represented as the data communication time matrix and task computation time matrix, a distribution over different task permutations by the improved Pointer network can be predicted. Then, the heuristic algorithm based on the HEFT achieves the task allocation to get the task executive time. Using negative task executive time as the reward signals, the model parameters by a policy gradient method in the first phase can be optimized. The simulation experiment is done from the task executive time, and the results shows that the workflow scheduling by the deep reinforcement learning is more effective comparing with other four single objective heuristic algorithms.