Deep Reinforcement Learning for Dynamic Workflow Scheduling in Cloud Environment
Tingting Dong, Fei Xue, Changbai Xiao, Jiangjiang Zhang · 2021
As a service-oriented parallel distributed computing paradigm, cloud computing can tackle large-scale computing problem by cloud resources. A challenge to optimize cloud resource utilization is more efficient scheduling users’ requests (workflows). However, most of algorithms assume that cloud resources’ performance is always fixed, which is impractical due to the uncertainty during the task execution. In this paper, workflow scheduling considering the performance variation of cloud resources is studied aiming to minimize the makespan, which is formulated as a Markov Decision Process. And, a dynamic workflow scheduling approach based on deep reinforcement learning (RLWS) is proposed. In this approach, a complete solution is as the input, and neural network parameters are learned by iteratively local re-scheduling to optimize the solution. Actor critic in deep reinforcement learning is designed to train the neural network parameters by self-learning procedure. Experiment results confirm that the proposed algorithm can efficiently shorten the makespan.