Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm

Abstract

The resource scheduling strategy of container cloud system plays an important role in resource utilization and cluster performance.The existing container cluster scheduling does not fully take into account the resource occupancy within and between nodes,which is prone to container resource bottlenecks,resulting in low resource utilization and poor service reliability.In order to balance the workload of container cluster and reduce the bottleneck of container resources,this paper proposes a container cluster scheduling optimization algorithm CS-DQN(container scheduling optimization strategy based on DQN)based on deep Q-lear-ning network(DQN).Firstly,an optimization model of container cluster resource utilization for load balancing is proposed.Then,using the deep reinforcement learning method,a container cluster scheduling algorithm based on DQN is designed,and the relevant state space,action space and reward function are defined.By introducing the improved DQN algorithm,the container dynamic scheduling strategy which meets the optimization goal is generated based on the self-learning method.The prototype experimental results show that the scheduling strategy expands the scale of deployable containers in scheduling,achieves better load balancing in different workloads,improves resource utilization,and the service reliability is better guaranteed.

Keywords

• container cloud|deep q-learning network|cluster|scheduling strategy