AI 中文总结
该研究针对边缘计算环境中状态更新与任务决策的异步问题，提出CoSMO框架，通过协作式强化学习实现语义状态管理与任务卸载的协同，显著提升了任务准时完成率和容量感知决策准确率。
AI 中文摘要
在边缘-云协同的边缘计算环境中，边缘节点（EN）必须决定每个用户任务是在本地执行、转发到远程服务（或云）节点（SN），还是被拒绝。EN可直接观测自身的本地状态，但仅能通过间歇性更新的缓存获取SN的状态，因此状态更新与任务控制在部分可观测下形成异步闭环。以信息年龄（AoI）为代表的新鲜度驱动方案未直接根据更新对后续任务决策的影响来评估其价值。本文提出CoSMO（语义状态管理与卸载协同设计），这是一种协作式事件驱动强化学习（RL）框架，通过已实现的任务效用协调语义状态管理与选择性卸载。CoSMO学习异构SN服务状态的紧凑表示；在SN端，循环半马尔可夫双深度Q网络（Double DQN）智能体共同选择发送/不发送操作及下一个决策间隔；在EN端，任务终端离策略值学习智能体基于本地观测和过时的远程语义做出分层门控-路由决策。各智能体维护独立的观测和值目标，但共享同一已实现的任务效用流，无需集中式执行。在评估的工作负载族中，CoSMO的准时完成率相较于表现最优的对比方法平均提升18.6%至21.2%；在三个严格过载点下的容量感知决策准确率方面，对应提升幅度平均为17.6%至17.9%。
英文摘要
In an edge--cloud collaborative edge-computing environment, an edge node (EN) must decide whether each user task should be executed locally, forwarded to a remote service (or cloud) node (SN), or rejected. The EN observes its local state directly but receives the SN state only through an intermittently refreshed cache. Status updating and task control therefore form an asynchronous closed loop under partial observability. Freshness-driven schemes, including those based on Age of Information (AoI), do not directly value an update by its effect on subsequent task decisions. We propose CoSMO (Co-design of Semantic-state Management and Offloading), a cooperative event-driven reinforcement learning (RL) framework that coordinates semantic status management and selective offloading through realized task utility. CoSMO learns a compact representation of the heterogeneous SN service state. At the SN, a recurrent semi-Markov double deep Q-network (Double DQN) agent jointly selects send/no-send and the next decision interval. At the EN, a task-terminal off-policy value-learning agent makes hierarchical gate--route decisions from local observations and stale remote semantics. The agents maintain separate observations and value targets but share the same realized task-utility stream, without centralized execution. Across the evaluated workload families, CoSMO's reported relative improvement in on-time completion rate over the best-performing competing method averages 18.6%--21.2%. For capacity-aware decision accuracy across the three strict-overload points, the corresponding reported gains average 17.6%--$17.9%.
Comments16 pages, 13 figures
