Network services are increasingly managed by considering chained-up virtual network functions and relevant traffic flows, known as the Service Function Chains (SFCs). To deal with sequential arrivals of SFCs in an online fashion, we must consider two closely-coupled problems - an SFC placement problem that maps SFCs to servers/links in the network and an SFC scheduling problem that determines when each SFC is executed. Solving the whole SFC problem targeting these two optimizations jointly is extremely challenging. In this paper, we propose a novel network diffuser using conditional generative modeling for this SFC placing-scheduling optimization. Recent advances in generative AI and diffusion models have made it possible to generate high-quality images/videos and decision trajectories from language description. We formulate the SFC optimization as a problem of generating a state sequence for planning and perform graph diffusion on the state trajectories to enable extraction of SFC decisions, with SFC optimization constraints and objectives as conditions. To address the lack of demonstration data due to NP-hardness and exponential problem space of the SFC optimization, we also propose a novel and somewhat maverick approach -- Rather than solving instances of this difficult optimization, we start with randomly-generated solutions as input, and then determine appropriate SFC optimization problems that render these solutions feasible. This inverse demonstration enables us to obtain sufficient expert demonstrations, i.e., problem-solution pairs, through further optimization. In our numerical evaluations, the proposed network diffuser outperforms learning and heuristic baselines, by $\sim$20\% improvement in SFC reward and $\sim$50\% reduction in SFC waiting time and blocking rate.

翻译：网络服务日益通过考虑链式虚拟网络功能及相关流量流进行管理，即服务功能链。为应对SFC的在线顺序到达，我们必须考虑两个紧密耦合的问题：将SFC映射到网络服务器/链路的放置问题，以及确定各SFC执行时间的调度问题。联合求解这两个优化目标的完整SFC问题极具挑战性。本文提出一种基于条件生成建模的新型网络扩散器，用于SFC放置-调度优化。生成式人工智能与扩散模型的最新进展使得从语言描述生成高质量图像/视频及决策轨迹成为可能。我们将SFC优化问题构建为生成规划状态序列的问题，通过在状态轨迹上执行图扩散来提取SFC决策，并以SFC优化约束和目标作为条件。针对SFC优化因NP难特性及指数级问题空间导致的演示数据缺乏问题，我们同时提出一种新颖且非传统的方法：不直接求解该困难优化问题的实例，而是以随机生成的解作为输入，进而确定使这些解可行的对应SFC优化问题。这种逆向演示方法通过进一步优化，使我们能够获得充足的专家演示数据（即问题-解对）。数值评估表明，所提出的网络扩散器在SFC奖励方面较学习与启发式基线提升约20%，在SFC等待时间与阻塞率方面降低约50%。