GAP: Graph attention-based PPO for low-latency layer-aware container migration in meta computing

Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many meta computers. In meta computing, tasks can be assigned to containers at edge nodes for processing, based on container images with multiple layers. The dynamic and resource-constrained nature of meta computing environments requires an optimal container migration strategy for mobile users to minimize latency. However, the problem of container migration in meta computing has not been thoroughly explored. To address this gap, we present Graph Attention-based PPO (GAP) algorithm that consider both proactive and passive migration. Specifically: (1) We formulate the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and task duration by considering four delays. (2) We introduce a reinforcement learning algorithm based on policy gradients to minimize total latency by identifying layer dependencies and topological relationships between users and node for action selection, making decisions for both proactive and passive migration. (3) Experiments using real data trajectories show that the algorithm outperforms baseline algorithms, achieving average 15% lower latency than other baselines.