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Hybrid content caching for low end-to-end latency in cloud-based wireless networks

机译:混合内容缓存可降低基于云的无线网络中的低端到端延迟

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In this paper, we consider the content caching design without requiring historical content access information or content popularity profiles in a hierarchical cellular network architecture. Our design aims to dynamically select caching locations for different contents where caching locations can be content servers, cloud units (CUs), and base stations (BSs). Our design objective is to support as high content request rates as possible while maintaining the finite service time. To tackle this design problem, we employ the Lyapunov optimization method where the caching algorithm is developed by minimizing the Lyapunov drift of a quadratic Lyapunov function of virtual queue backlogs. This solution approach requires to solve a max-weight problem, which is an NP-hard and difficult problem to solve due to the coupling between CU caching and BS caching decisions. By exploiting the submodularity of the objective function, we propose a hybrid caching algorithm which achieves the constant approximation ratio to the optimal performance. Trace-driven simulation results demonstrate that the proposed joint CU/BS caching algorithm achieves almost the same performance with the exhaustive search and outperforms the independent caching algorithm and heuristic joint caching algorithms in terms of average end-to-end latency and backhaul load reduction ratio.
机译:在本文中,我们考虑了在分层蜂窝网络体系结构中不需要历史内容访问信息或内容受欢迎程度配置文件的内容缓存设计。我们的设计旨在为不同的内容动态选择缓存位置,其中缓存位置可以是内容服务器,云单元(CU)和基站(BS)。我们的设计目标是在保持有限服务时间的同时,尽可能提高内容请求率。为了解决此设计问题,我们采用了Lyapunov优化方法,该方法通过最小化虚拟队列积压的二次Lyapunov函数的Lyapunov函数来开发缓存算法。此解决方案方法需要解决最大权重问题,由于CU缓存和BS缓存决策之间的耦合,因此这是一个难解决的NP问题。通过利用目标函数的子模量,我们提出了一种混合缓存算法,该算法可实现恒定的近似比率以达到最佳性能。跟踪驱动的仿真结果表明,所提出的联合CU / BS缓存算法与穷举搜索几乎实现了相同的性能,并且在平均端到端延迟和回程负载减少率方面优于独立缓存算法和启发式联合缓存算法。 。

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