Global Microservice Autoscaling Over Heterogeneous Edge Environments for Internet Applications: A Reinforcement Learning Approach

The integration of microservice architecture and edge computing offers innovative solutions for highly interactive, low-latency Internet applications. To manage the dynamic nature of requests in edge computing, microservice autoscaling techniques are frequently employed. However, the resource limitation of individual edge servers and the heterogeneity among edge servers present significant challenges for autoscaling in edge computing. Meanwhile, few studies have considered the long-term optimization and the joint optimization of instance adjustment and request routing in edge computing. This article aims to fill these gaps. First, we propose global horizontal pod autoscaler (GHPA), a novel framework that addresses microservice autoscaling from the perspective of edge server clusters. Second, we consider the joint optimization of instance adjustment and request routing, and formulate a long-term optimization problem. Third, we transform the long-term optimization problem into a Markov Decision Problem (MDP) and use reinforcement learning techniques to solve it. Finally, we conduct extensive experiments using both real and synthetic data. The experiment results demonstrate that our algorithm achieves at least a 10% performance improvement in various test environments compared to state-of-the-art algorithms.