The proliferation of IoT devices has led to a surge in network traffic, resulting in higher energy usage and response delays. In-network caching has emerged as a viable solution to address this issue. However, caching IoT data faces two key challenges: the transient nature of IoT content and the unknown spatiotemporal content popularity. Additionally, the use of a global view on dynamic IoT networks is problematic due to the high communication overhead involved. To tackle these challenges, this paper presents an adaptive management approach that jointly optimizes caching and communication in IoT networks using a novel bi-level control method called BC3. The approach employs two types of controllers: a global ILP-based optimal controller for long-term timeslots and local learning-based controllers for short-term timeslots. The long-term controller periodically establishes a global cache policy for the network and sends specific cache rules to each edge server. The local controller at each edge server solves the joint problem of bandwidth allocation and cache adaptation using deep reinforcement learning (DRL) technique. The main objective is to minimize energy consumption and system response time with utilizing the global and local observations. Experimental results demonstrate that the proposed approach increases cache hit rate by approximately 12% and uses 11% less energy compared to the other methods. Increasing the cache hit rate can lead to a reduction in about 17% in response time for user requests. Our bi-level control approach offers a promising solution for leveraging the network's global visibility while balancing communication overhead (as energy consumption) against system performance. Additionally, the proposed method has the lowest cache eviction, around 19% lower than the lowest eviction of the other comparison methods. The eviction metric is a metric to evaluate the effectiveness of adaptive caching approach designed for transient data.

中文翻译：

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一个混合双级管理框架，用于在支持 Edge-AI 的 IoT 中进行缓存和通信


物联网设备的激增导致网络流量激增，导致更高的能源使用和响应延迟。网络内缓存已成为解决此问题的可行解决方案。但是，缓存 IoT 数据面临两个关键挑战：IoT 内容的瞬态性质和未知的时空内容受欢迎程度。此外，由于涉及高通信开销，在动态 IoT 网络上使用全局视图是有问题的。为了应对这些挑战，本文提出了一种自适应管理方法，该方法使用一种称为 BC3 的新型双层控制方法联合优化 IoT 网络中的缓存和通信。该方法采用两种类型的控制器：用于长期时隙的基于 ILP 的全局最佳控制器和用于短期时隙的基于本地学习的控制器。长期控制器会定期为网络建立全局缓存策略，并将特定的缓存规则发送到每个边缘服务器。每个边缘服务器的本地控制器使用深度强化学习 （DRL） 技术解决了带宽分配和缓存适应的联合问题。主要目标是利用全球和本地观测来最大限度地减少能源消耗和系统响应时间。实验结果表明，与其他方法相比，所提出的方法将缓存命中率提高了约 12%，能耗降低了 11%。提高缓存命中率可以使用户请求的响应时间减少约 17%。我们的双层控制方法提供了一种很有前途的解决方案，可以利用网络的全球可见性，同时平衡通信开销（如能耗）与系统性能。 此外，所提出的方法具有最低的缓存驱逐率，比其他比较方法的最低驱逐率低约 19%。驱逐指标是评估为瞬态数据设计的自适应缓存方法有效性的指标。