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.

翻译：随着小小区网络用户对计算密集型服务的依赖日益增加，基于云端的访问往往导致高延迟。多接入边缘计算通过将计算资源部署至靠近终端用户的位置（以小基站作为边缘服务器来支持低延迟服务交付）缓解了这一问题。然而，在服务需求未知且网络状态动态变化的情况下，有限的边缘容量使得在本地部署服务与云端部署之间做出决策极具挑战性。为此，本文构建服务需求与服务属性之间的线性函数关系，并将服务部署任务建模为线性赌博机问题：以小基站为智能体、服务为臂，目标在于识别出部署在边缘时相比云端部署能最大程度降低用户总延迟的服务。我们提出了一种固定置信度设定下的分布式自适应多智能体最优臂识别算法，该算法通过小基站间的协作加速学习过程。仿真结果表明，该算法能以预期置信度识别最优服务，且其学习轮次与小基站数量呈反比例减少，实现了接近最优的加速比。此外，我们对该算法的样本复杂度和通信开销进行了理论分析。