  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 low-latency, layer-aware container migration strategies 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 overall 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 for
action selection, making decisions for both proactive and passive migration.
Expert demonstrations are introduced to enhance exploitation. 3) Experiments
using real data trajectories show that the algorithm outperforms baseline
algorithms, achieving lower total latency.
