In multi-access edge computing (MEC) systems, there are multiple local cache servers caching contents to satisfy the users' requests, instead of letting the users download via the remote cloud server. In this paper, a multi-cell content scheduling problem (MCSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate local cache servers, and thus the caching decisions in all cells must be jointly optimized. We first prove that MCSP is NP-hard, then we formulate MCSP using integer linear programming, by which the optimal scheduling can be obtained for small-scale instances. For problem solving of large scenarios, via a mathematical reformulation, we derive a scalable optimization algorithm based on repeated column generation. Our performance evaluation shows the effectiveness of the proposed algorithm in comparison to an off-the-shelf commercial solver and a popularity-based caching.
翻译:在多存取边缘计算(MEC)系统中,有多个本地缓存服务器缓存内容,以满足用户的要求,而不是让用户通过远程云端服务器下载。在本文中,考虑到MEC系统中的多细胞内容排期问题。考虑到缓存内容的新鲜性和交通数据成本,我们研究如何在多细胞环境中同时安排内容更新。与单细胞情景不同的是,用户可能拥有多个候选本地缓存服务器,因此所有单元格的缓存决定必须共同优化。我们首先证明 MCSP 是硬的,然后我们用整线编程制定MCSP,从而可以对小规模案例取得最佳的排期。对于解决大型情景的问题,我们通过重新进行数学调整,根据重复的列生成,得出一个可缩放的优化算法。我们的绩效评估显示,与现成的商业求解码和广域化缓存相比,拟议的算法是有效的。