Mobile edge computing (MEC) can pre-cache deep neural networks (DNNs) near end-users, providing low-latency services and improving users' quality of experience (QoE). However, caching all DNN models at edge servers with limited capacity is difficult, and the impact of model loading time on QoE remains underexplored. Hence, we introduce dynamic DNNs in edge scenarios, disassembling a complete DNN model into interrelated submodels for more fine-grained and flexible model caching and request routing solutions. This raises the pressing issue of jointly deciding request routing and submodel caching for dynamic DNNs to balance model inference precision and loading latency for QoE optimization. In this paper, we study the joint dynamic model caching and request routing problem in MEC networks, aiming to maximize user request inference precision under constraints of server resources, latency, and model loading time. To tackle this problem, we propose CoCaR, an offline algorithm based on linear programming and random rounding that leverages dynamic DNNs to optimize caching and routing schemes, achieving near-optimal performance. Furthermore, we develop an online variant of CoCaR, named CoCaR-OL, enabling effective adaptation to dynamic and unpredictable online request patterns. The simulation results demonstrate that the proposed CoCaR improves the average inference precision of user requests by 46% compared to state-of-the-art baselines. In addition, in online scenarios, CoCaR-OL achieves an improvement of no less than 32.3% in user QoE over competitive baselines.

翻译：移动边缘计算（MEC）可在终端用户附近预缓存深度神经网络（DNN），以提供低延迟服务并提升用户体验质量（QoE）。然而，在有限容量的边缘服务器上缓存所有DNN模型存在困难，且模型加载时间对QoE的影响尚未被充分探究。为此，我们引入动态DNN至边缘场景，将完整DNN模型拆解为相互关联的子模型，以实现更细粒度且灵活的模型缓存与请求路由方案。这引发了一个紧迫问题：需联合决策动态DNN的请求路由与子模型缓存，以平衡模型推理精度与加载延迟，从而优化QoE。本文研究MEC网络中的联合动态模型缓存与请求路由问题，目标是在服务器资源、延迟及模型加载时间约束下最大化用户请求的推理精度。为应对该问题，我们提出CoCaR算法——一种基于线性规划与随机舍入的离线算法，通过利用动态DNN优化缓存与路由策略，实现近最优性能。此外，我们进一步开发了CoCaR的在线变体CoCaR-OL，可有效适应动态不可预测的在线请求模式。仿真结果表明，与最先进基线方法相比，所提CoCaR将用户请求的平均推理精度提升46%。在在线场景中，CoCaR-OL实现的用户QoE提升相较于竞争基线不低于32.3%。