Optimization of Task-Scheduling Strategy in Edge Kubernetes Clusters Based on Deep Reinforcement Learning

Abstract

Kubernetes, known for its versatility in infrastructure management, rapid scalability, and ease of deployment, makes it an excellent platform for edge computing. However, its native scheduling algorithm struggles with load balancing, especially during peak task deployment in edge environments characterized by resource limitations and low latency demands. To address this issue, a proximal policy optimization with the least response time (PPO-LRT) algorithm was proposed in this paper. This deep reinforcement learning approach learns the pod-scheduling process, which can adaptively schedule edge tasks to the most suitable worker nodes with the shortest response time according to the current cluster load and pod state. To evaluate the effectiveness of the proposed algorithm, multiple virtual machines were created, and we built a heterogeneous node cluster. Additionally, we deployed k3s, a Kubernetes distribution suited for edge environments, on the cluster. The load balancing, high load resilience, and average response time during peak task deployment were tested by initiating numerous tasks within a limited time frame. The results validate that the PPO-LRT-based scheduler shows superior performance in cluster load balancing compared to the Kube scheduler. After the deployment of 500 random tasks, several cluster nodes become overwhelmed by using the Kube scheduler, whereas the PPO-LRT-based scheduler evenly allocates the workload across the cluster, reducing the average response time by approximately 31%.