Mobile edge computing is a key technology for the future wireless networks and hence, the efficiency of a cache-placement algorithm is important to seek the cache content which satisfies the maximum user demands. Since recommendations personalizes an individual's choices, it is responsible for a significant percentage of user requests, and hence recommendation can be utilized to maximize the overall cache hit rate. Hence, in this work, joint optimization of both recommendation and caching is proposed. The influence of recommendation on the popularity of a file is modelled using a conditional probability distribution. To this end, the concept of probability matrix is introduced and a Bayesian based model, specifically Dirichlet distribution is used to predict and estimate the content request probability and hence the average cache hit is derived. Joint recommendation and caching algorithm is presented to maximize the average cache hits. Subsequently, theoretical guarantees are provided on the performance of the algorithm. Also, a heterogeneous network consisting of M small base stations and one macro base station is also presented. Finally, simulation results confirm the efficiency of the proposed algorithms in terms of average cache hit rate, delay and throughput.
翻译:移动边缘计算是未来无线网络的关键技术,因此,缓存定位算法的效率对于寻找满足最大用户需求的缓存内容十分重要。由于建议是个人个人选择,因此它负责很大一部分用户请求,因此建议可以用于最大限度地提高总体缓存点击率。因此,在这项工作中,提出了联合优化建议和缓存的建议。建议对文件受欢迎程度的影响是使用有条件的概率分布模型模拟的。为此,引入了概率矩阵概念,并采用了基于巴耶斯模式的概率矩阵概念,特别是Drichlet分布模型,用于预测和估计内容请求的概率,从而得出平均缓存点击率。提出了联合建议和缓存算法,以尽量扩大平均缓存点击率。随后,对算法的性能提供了理论保证。此外,还介绍了由M小基地站和一个宏观基地站组成的混合网络。最后,模拟结果证实了拟议算法在平均缓存点击率、延迟和吞吐量方面的效率。