元计算中低延迟、感知层的主动与被动容器迁移
Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing
AI总结：
针对元计算中容器迁移研究不足的问题，本文提出考虑层依赖、融合主动与被动迁移的低延迟策略，采用带专家演示的策略梯度强化学习算法优化决策，实验验证其相比基准算法能有效降低总延迟。
摘要：
元计算是一种新型计算范式，旨在高效利用所有网络计算资源，提供具备强安全与隐私保障的容错个性化服务，同时致力于将互联网虚拟化为多台元计算机。在元计算中，任务可被分配至边缘节点的容器处理，这些容器基于包含多层的容器镜像运行。元计算环境的动态性与资源受限特性，需要为移动用户设计最优容器迁移策略以最小化延迟，但元计算中的容器迁移问题尚未得到充分研究。为填补这一空白，本文提出了同时考虑主动与被动迁移的低延迟、感知层的容器迁移策略。具体而言：1）本文对元计算中的容器迁移问题进行建模，考虑层依赖关系，通过纳入四类延迟来降低迁移成本与总任务时长。2）提出一种基于策略梯度的强化学习算法，通过识别层依赖关系进行动作选择，同时为主动和被动迁移做出决策，以最小化总延迟；还引入了专家演示以增强利用效率。3）基于真实轨迹数据的实验表明，该算法性能优于基准算法，可实现更低的总延迟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
