Multi-Objective DRL for Efficient Kubernetes Microservice Scheduling
TL;DR
This study formulates the multi-objective microservice deployment problem in Kubernetes, balancing service latency and resource utilization, and proposes a deep reinforcement learning method with reward shaping and heuristic scaling to derive optimal deployment policies.
Abstract
Container based microservice architecture is becoming the priority of service development in edge computing. Its flexibility and scalability could provide stable service response. However, due to the limited computing resources of each edge node in edge computing, how to use computing resources efficiently has become an important issue in microservice deployment. This study formulates the multi-objective microservice deployment problem (MMDP) in Kubernetes, balancing service latency and resource utilization. We propose a deep reinforcement learning method with reward shaping and heuristic scaling to derive optimal deployment policies. Experiments show that our approach reduces response time by 25–30% and improves load balancing by 20% compared with Kubernetes default scheduling and DQL. These results demonstrate the applicability of reinforcement learning for distributed computing and automatic control in microservice systems.