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A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation

机译:一种新型移动边缘网络架构,具有联合缓存交付和横向合作

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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 problemby 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 problema 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 problemto 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.
机译:移动边缘缓存/计算(MEC)已成为有前途的范式,以便在未来的无线网络中提供超高速率,超可靠和/或低延迟通信。在本文中,我们介绍了一种新的MEC网络架构,它利用了移动边缘节点(MENs)之间的横向合作的最佳联合缓存提供。为此,我们首先制定联合优化内容缓存和在各个男性的各种网络限制下提供决策的内容访问延迟最小化问题问题。然而,决策之间的强烈依赖性使得问题嵌套的双重优化被证明是NP-HARD。要处理它,我们提出了一种新颖的转换方法来将嵌套的双重问题转换为等效的混合整数非线性编程(MINLP)优化问题。然后,我们使用带有内部点方法的改进的分支和绑定算法设计集中解决方案,找到联合缓存和交付策略,这在最佳解决方案的1%以内。由于集中式解决方案要求所有男士的完整网络拓扑和信息,以使我们的解决方案可扩展,我们开发了一种分布式算法,允许每个人根据其当地观察来创建自己的决策。广泛的模拟表明,与其他当前缓存策略相比,该解决方案可以将整个网络的总平均延迟降低至40%。此外,所提出的解决方案还将网络的高速缓存命中率提高了4倍,从而大大减少了回程网络上的流量负载。

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