标题： 面向网络切片供应的分层放置学习 显示英文标题

标题： Hierarchical Placement Learning for Network Slice Provisioning

摘要： 在本工作中，我们旨在解决基于边缘的移动网络中的切片供应挑战。我们提出了一种解决方案，用于学习服务功能链的放置策略，以最大化请求接受率，同时最小化平均节点资源利用率。为此，我们考虑了一个分层多臂老虎机问题，并提出了一种两层分层老虎机解决方案，旨在在线方式下学习一种可扩展的放置策略，以优化上述目标。在两个真实网络拓扑上的仿真显示，与基线方法相比，我们的方法在某些情况下实现了5%的平均节点资源利用率，同时接纳了超过25%的切片请求。 显示英文摘要

摘要： In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated objectives in an online manner. Simulations on two real network topologies show that our proposed approach achieves 5% average node resource utilization while admitting over 25% more slice requests in certain scenarios, compared to baseline methods.