# Energy-Efficient Dynamic Training and Inference for GNN-Based Network Modeling

arXiv: https://arxiv.org/abs/2503.18706 · HTML: https://mlsanity.com/paper/2503.18706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Benchmarking TPU, GPU, and CPU Platforms for Deep Learning](https://mlsanity.com/paper/1907.10701/) (2019)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems](https://mlsanity.com/paper/2404.08060/) (2024)
- [Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks](https://mlsanity.com/paper/2410.16723/) (2024)

## Abstract (arXiv)

Efficient network modeling is essential for resource optimization and network planning in next-generation large-scale complex networks. Traditional approaches, such as queuing theory-based modeling and packet-based simulators, can be inefficient due to the assumption made and the computational expense, respectively. To address these challenges, we propose an innovative energy-efficient dynamic orchestration of Graph Neural Networks (GNN) based model training and inference framework for context-aware network modeling and predictions. We have developed a low-complexity solution framework, QAG, that is a Quantum approximation optimization (QAO) algorithm for Adaptive orchestration of GNN-based network modeling. We leverage the tripartite graph model to represent a multi-application system with many compute nodes. Thereafter, we apply the constrained graph-cutting using QAO to find the feasible energy-efficient configurations of the GNN-based model and deploying them on the available compute nodes to meet the network modeling application requirements. The proposed QAG scheme closely matches the optimum and offers atleast a 50% energy saving while meeting the application requirements with 60% lower churn-rate.