Edge-cloud convergence is reshaping service provisioning across 5G/6G and computing power networks (CPNs). Service function chaining (SFC) requires continuously placing and scheduling virtual network functions (VNFs) chains under compute/bandwidth and end-to-end QoS constraints. Most SFC optimizers assume static or stationary networks, and degrade under long-term topology/resource changes (failures, upgrades, expansions) that induce non-stationary graph drifts. We propose LiSFC, a Lipschitz lifelong planner that transfers MCTS statistics across drifting network configurations using an MDP-distance bound. More precisely, we formulate the problem as a sequence of MDPs indexed by the underlying network graph and constraints, and we define a \emph{graph drift} metric that upper-bounds the LiZero MDP distance. This allows LiSFC to import theoretical guarantees on bias and sample efficiency from the LiZero framework while being tailored to cloud-network convergence. We then design \emph{LiSFC-Search}, an SFC-aware unified MCTS (UMCTS) procedure that uses transferable adaptive UCT (aUCT) bonuses to reuse search statistics from prior CPN configurations. Preliminary results on synthetic CPN topologies and SFC workloads show that LiSFC consistently reduces SFC blocking probability and improves tail delay compared to non-transfer MCTS and purely learning-based baselines, highlighting its potential as an AI/ML building block for cloud-network convergence.

翻译：边缘云融合正在重塑5G/6G与算力网络中的服务供给模式。服务功能链需要在计算/带宽资源与端到端服务质量约束下，持续部署和调度虚拟网络功能链。现有大多数SFC优化器基于静态或稳态网络假设，在长期拓扑/资源变化（故障、升级、扩容）引发的非稳态图漂移场景下性能显著退化。本文提出LiSFC——一种基于Lipschitz约束的终身规划器，通过MDP距离上界实现蒙特卡洛树搜索统计量在漂移网络配置间的迁移。具体而言，我们将该问题建模为以底层网络图及约束为索引的马尔可夫决策过程序列，并定义可上界LiZero MDP距离的图漂移度量。这使得LiSFC在继承LiZero框架偏差与样本效率理论保证的同时，能适配云网融合场景。进而设计SFC感知的统一MCTS流程LiSFC-Search，采用可迁移的自适应UCT奖励机制复用历史算力网络配置的搜索统计量。在合成算力网络拓扑与SFC工作负载上的初步实验表明：相较于无迁移MCTS及纯学习基线方法，LiSFC能持续降低SFC阻塞概率并改善尾部延迟，彰显其作为云网融合AI/ML组件的潜力。