Mobile edge caching/computing (MEC) has been emerging as a promising paradigm to provide ultra-high rate, ultra-reliable, and/or low-latency communications in future wireless networks. In this paper, we introduce a novel MEC network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs). To that end, we first formulate the content-access delay minimization problem by jointly optimizing the content caching and delivering decisions under various network constraints (e.g., network topology, storage capacity and users’ demands at each MEN). However, the strongly mutual dependency between the decisions makes the problem a nested dual optimization that is proved to be NP-hard. To deal with it, we propose a novel transformation method to transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) optimization problem. Then, we design a centralized solution using an improved branch-and-bound algorithm with the interior-point method to find the joint caching and delivering policy which is within 1 percent of the optimal solution. Since the centralized solution requires the full network topology and information from all MENs, to make our solution scalable, we develop a distributed algorithm which allows each MEN to make its own decisions based on its local observations. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40 percent compared with other current caching policies. Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network.

中文翻译：

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一种具有联合缓存交付和横向协作的新型移动边缘网络架构

移动边缘缓存/计算 (MEC) 已成为在未来无线网络中提供超高速率、超可靠和/或低延迟通信的有前途的范例。在本文中，我们介绍了一种新颖的 MEC 网络架构，该架构利用移动边缘节点 (MEN) 之间的横向合作来优化联合缓存交付。为此，我们首先通过在各种网络约束（例如，网络拓扑、存储容量和每个 MEN 的用户需求）下联合优化内容缓存和交付决策来制定内容访问延迟最小化问题。然而，决策之间的强烈相互依赖使问题成为嵌套对偶优化，被证明是 NP-hard。对付它，我们提出了一种新的转换方法，将嵌套对偶问题转换为等效的混合整数非线性规划 (MINLP) 优化问题。然后，我们使用改进的分支定界算法和内点方法设计了一个集中式解决方案，以找到与最佳解决方案相差在 1% 以内的联合缓存和交付策略。由于集中式解决方案需要来自所有 MEN 的完整网络拓扑和信息，为了使我们的解决方案具有可扩展性，我们开发了一种分布式算法，允许每个 MEN 根据其本地观察做出自己的决策。大量模拟表明，与其他当前缓存策略相比，所提出的解决方案可以将整个网络的总平均延迟降低多达 40%。此外，