标题： 使用线性多臂老虎机中的分布式最佳臂识别进行小小区网络服务部署 显示英文标题

标题： Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits

摘要： 随着小型基站网络中的用户越来越多地依赖计算密集型服务，基于云的接入通常会导致高延迟。 多接入边缘计算（MEC）通过将计算资源更接近终端用户来缓解这一问题，其中小型基站（SBSs）作为边缘服务器以实现低延迟的服务交付。 然而，有限的边缘容量使得在本地与云端之间决定部署哪些服务变得具有挑战性，尤其是在服务需求未知和网络条件动态变化的情况下。 为了解决这个问题，我们将服务需求建模为服务属性的线性函数，并将服务放置任务建模为一个线性老虎机问题，其中SBSs作为智能体，服务作为手臂。 目标是确定当放置在边缘时，相较于云端部署能最大程度减少总用户延迟的服务。 我们在固定置信度设置下提出了一种分布式且自适应的多智能体最优手臂识别（BAI）算法，其中SBSs协作以加速学习。 仿真结果显示，我们的算法能够在所需置信度下识别出最佳服务，并实现接近最优的速度提升，随着学习轮次的数量与SBSs的数量成比例减少。 我们还提供了该算法的样本复杂性和通信开销的理论分析。 显示英文摘要

摘要： As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dynamic network conditions. To tackle this problem, we model service demand as a linear function of service attributes and formulate the service placement task as a linear bandit problem, where SBSs act as agents and services as arms. The goal is to identify the service that, when placed at the edge, offers the greatest reduction in total user delay compared to cloud deployment. We propose a distributed and adaptive multi-agent best-arm identification (BAI) algorithm under a fixed-confidence setting, where SBSs collaborate to accelerate learning. Simulations show that our algorithm identifies the optimal service with the desired confidence and achieves near-optimal speedup, as the number of learning rounds decreases proportionally with the number of SBSs. We also provide theoretical analysis of the algorithm's sample complexity and communication overhead.