MO-DDPG: An Affinity and Anti-Affinity-Based Container Service Migration Strategy in MEC

The time-varying characteristics of user mobility, node and services connections, and edge resources in Mobile Edge Computing (MEC) scenarios pose significant challenges for designing efficient service migration strategies to enhance system performance. This paper introduces and quantifies affinity and anti-affinity metrics to evaluate the discrepancies between the resource requirements of containers and the available resources of nodes, as well as the competition level of computing resources during the migration of containers among different nodes within the Kubernetes cluster. To adapt to the complexity of the edge environment, such metrics are integrated into the reward update mechanism of the Deep Deterministic Policy Gradient (DDPG) reinforcement algorithm. Besides, the Multi-Objective Evolutionary Algorithm (MOEA) is employed to dynamically adjust the weights of various reward objectives, forming a self-adaptive online container migration strategy named MO-DDPG. Finally, we construct a real-world heterogeneous Kubernetes edge node cluster in experiments and use a public dataset to simulate multi-modal connections between mobile user trajectories and service demands. Compared to the greedy and heuristic strategies that consider only single metrics, our experiment results show that the proposed strategy improves energy efficiency and reduces latency by 21.30% and 29.49%, respectively. Moreover, the MO-DDPG improves resource utilization of the node cluster compared to the default Kubernetes scheduler.