Data Centers Job Scheduling with Deep Reinforcement Learning
This addresses efficient resource allocation in data centers, an incremental improvement for data center management.
The paper tackles job scheduling in data centers under heterogeneous complexity by proposing A2cScheduler, an Advantage Actor-Critic deep reinforcement learning approach, and shows it achieves competitive performance on simulated and real workloads.
Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.