Performance and Cost-Aware Task Scheduling via Deep Reinforcement Learning in Cloud Environment
This work focus on solve the task scheduling problem in cloud computing datacenter with deep reinforcement learning method. Specifically, we adopted the DDPG algorithm to schedule multiple incoming tasks simultaneously, which considers the correlation within the incoming batch of tasks. The experiment results on Alibaba cloud dataset reveal the optimal performance of this method.