用量子优化动态调度GNN模型，节能超50%且稳定运行
Energy-Efficient Dynamic Training and Inference for GNN-Based Network Modeling
- 基于量子近似算法动态调度GNN训练与推理
- 相比传统方法节能超50%，切换率降低60%
- 适合大规模网络建模中的低功耗部署场景
下一代大规模复杂网络的资源优化与网络规划亟需高效建模。传统方法如基于排队论的建模和包级仿真器存在假设局限或计算开销大等问题。为此，本文提出一种面向上下文感知网络建模与预测的图神经网络（GNN）动态训练与推理能量高效协同框架。设计了低复杂度的QAG方案——基于量子近似优化（QAO）的自适应协同算法，采用三部分图模型表示包含多个计算节点的多应用系统。通过约束图切割技术，利用QAO寻找满足应用需求的能源高效配置，并在可用计算节点上部署。所提QAG方案接近最优解，在满足应用要求的同时实现至少50%的能耗降低，且故障切换率下降60%。
原文摘要 · Abstract (English)
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.
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