以用户为中心的MEC中基站分簇和业务缓存的联合优化
Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC
摘要：
边缘服务缓存可以有效地减少获取和初始化应用程序的延迟或带宽开销。针对传统蜂窝边缘服务缓存网络中存在的单基站传输受限和严重的边缘效应问题，提出了一种以用户为中心的边缘服务缓存框架，每个用户由一个特定的基站集群代替单个基站共同提供边缘缓存和无线传输服务.为了最小化缓存成本约束下的长期平均延迟，一个混合整数非线性规划（MINLP）问题，制定了联合优化BS分簇和服务缓存的决定。为了解决这个问题，我们提出了JO-CDSD，一个有效的联合优化算法的基础上李雅普诺夫优化和广义Benders分解（GBD）。特别地，长期优化问题可以被转换为每个时隙中的原始问题和主问题，这要简单得多。通过交替求解主问题和原始问题，可以得到近似最优的分簇和缓存策略。大量的仿真结果表明，所提出的联合优化算法优于其他算法，可以有效地减少最多$93.75% and caching cost by at most $ 53.12%的长期延迟。
英文摘要：
Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay by at most $93.75% and caching cost by at most $53.12%.
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