DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment

In the rapidly evolving landscape of cloud-edge computing, efficient resource scheduling across Kubernetes clusters is essential for optimizing microservice deployment. Traditional scheduling methods, e.g., heuristic and meta-heuristic algorithms, often struggle with the dynamic and heterogeneous nature of cloud-edge environments, relying on fixed parameters and lacking adaptability. We propose and implement DRKC, a novel deep reinforcement learning-based approach that addresses these challenges by improving resource utilization and balancing workloads. We model the scheduling problem as a Markov decision process, enabling DRKC to automatically learn optimal scheduling policies from real-time system data without relying on predefined heuristics. The work synthesizes state information from multiple clusters, using multidimensional resource awareness to effectively respond to changing conditions. We evaluate our performance in three Kubernetes clusters with thirteen nodes and ninety-six test applications with different resource requirements. Experimental results validate the effectiveness of DRKC in enhancing overall resource efficiency and achieving superior load balancing across cloud-edge environments.