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Mobility-Aware Video Prefetch Caching and Replacement Strategies in Mobile-Edge Computing Networks

机译:移动边缘计算网络中具有移动性的视频预取缓存和替换策略

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The drastic increase of mobile video traffic has resulted in communication delay and slow download speed. In the 5G wireless networks, caching or prefetching video at mobile edge servers can take advantage of the high-speed local link to alleviate the load of the core network. However, the tradeoff between data prefetching and cache replacement strategy is still a challenge in Mobile-edge computing networks, especially when the users are mobile and cache size is constrained. To address this, we design a mobility-aware utility function based on the user's moving probability and the popularity of video clips. Then, we formulate the optimization problem as an integer linear programming (ILP)with a set of knapsack constraints to develop an efficient dynamic programming algorithm. Compared with the baseline algorithm, the results of simulation experiment show that our approach can not only raise the hit rate by 24.93%, but also control the cost effectively. In the meantime, video distortion reduction is increased by 21.26% on average as well.
机译:移动视频流量的急剧增加导致通信延迟和下载速度缓慢。在5G无线网络中,在移动边缘服务器上缓存或预取视频可以利用高速本地链路来减轻核心网络的负载。但是,在移动边缘计算网络中,数据预取和缓存替换策略之间的权衡仍然是一个挑战,特别是当用户是移动用户并且缓存大小受到限制时。为了解决这个问题,我们基于用户的移动概率和视频片段的受欢迎程度设计了一种移动意识实用工具功能。然后,我们将优化问题公式化为具有背包约束的整数线性规划(ILP),以开发一种有效的动态规划算法。与基线算法相比,仿真实验结果表明,该方法不仅可以将命中率提高24.93%,而且可以有效地控制成本。同时,视频失真的减少平均也增加了21.26%。

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