# Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration

arXiv: https://arxiv.org/abs/2501.05673 · HTML: https://mlsanity.com/paper/2501.05673/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [Is Conditional Generative Modeling all you need for Decision-Making?](https://mlsanity.com/paper/2211.15657/) (2022)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Permutation Invariant Graph Generation via Score-Based Generative Modeling](https://mlsanity.com/paper/2003.00638/) (2020)
- [Learning to Poke by Poking: Experiential Learning of Intuitive Physics](https://mlsanity.com/paper/1606.07419/) (2016)
- [Inverse Optimization: Theory and Applications](https://mlsanity.com/paper/2109.03920/) (2021)
- [Modeling Other Players with Bayesian Beliefs for Games with Incomplete Information](https://mlsanity.com/paper/2405.14122/) (2024)
- [Collaborative AI Teaming in Unknown Environments via Active Goal Deduction](https://mlsanity.com/paper/2403.15341/) (2024)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [Shortest Path and Maximum Flow Problems Under Service Function Chaining Constraints](https://mlsanity.com/paper/1801.05795/) (2018)
- [SFC Deployment in Space-Air-Ground Integrated Networks Based on Matching Game](https://mlsanity.com/paper/2303.01020/) (2023)

## Built on by (most cited first, 11 of 11)

- [InSPO: Unlocking Intrinsic Self-Reflection for LLM Preference Optimization](https://mlsanity.com/paper/2512.23126/) (2025)
- [Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization](https://mlsanity.com/paper/2508.05995/) (2025)
- [A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality](https://mlsanity.com/paper/2508.09185/) (2025)
- [Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamics](https://mlsanity.com/paper/2602.06939/) (2026)
- [Operator-Guided Invariance Learning for Continuous Reinforcement Learning](https://mlsanity.com/paper/2605.06500/) (2026)
- [Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level Guarantees](https://mlsanity.com/paper/2508.05441/) (2025)
- [Metric-Gradient Projection for Stable Multi-Agent Policy Learning](https://mlsanity.com/paper/2605.18809/) (2026)
- [Matrix-Space Reinforcement Learning for Reusing Local Transition Geometry](https://mlsanity.com/paper/2605.14304/) (2026)
- [Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control](https://mlsanity.com/paper/2608.00908/) (2026)
- [Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry](https://mlsanity.com/paper/2609.26068/) (2026)
- [CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum](https://mlsanity.com/paper/2511.01343/) (2025)

## Abstract (arXiv)

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 ~20% improvement in SFC reward and ~50% reduction in SFC waiting time and blocking rate.