A New Approach for Resource Scheduling with Deep Reinforcement Learning
This work addresses resource scheduling problems, likely for cloud or data center management, but appears incremental as it builds on prior DRL research.
The authors tackled resource scheduling by introducing DeepRM2 and DeepRM_Off algorithms, which achieved faster convergence and better efficiency in metrics like average slowdown time and job completion time compared to existing DRL and heuristic methods.
With the rapid development of deep learning, deep reinforcement learning (DRL) began to appear in the field of resource scheduling in recent years. Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM and heuristic algorithms, our proposed algorithms have faster convergence speed and better scheduling efficiency with regarding to average slowdown time, job completion time and rewards.