1 research outputs found
Data Centers Job Scheduling with Deep Reinforcement Learning
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.Comment: 13 page