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.

翻译：元计算是一种新兴的计算范式，旨在高效利用所有网络计算资源，以提供具备强安全性与隐私保障的容错化、个性化服务，并致力于将互联网虚拟化为众多元计算机。在元计算中，任务可基于包含多层的容器镜像，分配至边缘节点的容器中进行处理。元计算环境的动态性与资源受限特性要求为移动用户设计最优的容器迁移策略以最小化延迟。然而，元计算中的容器迁移问题尚未得到深入探索。为填补这一空白，本文提出了低延迟、层感知的容器迁移策略，同时考虑主动迁移与被动迁移。具体而言：1）我们构建了元计算中的容器迁移问题模型，通过考虑四种延迟并纳入层间依赖关系，以降低迁移成本并缩短总体任务执行时间。2）我们引入一种基于策略梯度的强化学习算法，通过识别层依赖关系以指导动作选择，从而为主动与被动迁移做出决策，并引入专家示范以增强策略利用效率。3）基于真实数据轨迹的实验表明，该算法优于基线算法，实现了更低的总延迟。