Stackelberg game improves resource use in multi-band optical networks
Stackelberg Game with Multi-Agent Transformer for ISRS-Aware Resource Re-optimization in Multi-Band Optical Networks
Networking and Internet Architecture
Summary
Optical networks that connect AI data centers must constantly adjust how they allocate resources to handle changing traffic without blocking services. The authors show that nonlinear effects between channels make this adjustment complicated, as changing one service can affect others unpredictably. They propose a method that models these interactions using a game-based approach combined with advanced AI to select which services to adjust and how. Their method improves network resource use and reduces service blockages while keeping computation efficient.
What this means in practice
- •For optical network engineers: Improve dynamic resource allocation in multi-band optical networks to reduce service blockages and increase utilization during changing traffic conditions.
- •For data center network operators: Enhance connectivity management across interconnected AI data centers using more efficient channel allocation that accounts for nonlinear optical effects.
Authors
Jiaxin Liu, Rentao Gu, Miao Zhu, Zeguang Zou, Hui Li, Yuefeng Ji
Abstract
The rapid expansion of generative AI is driving computing infrastructures toward interconnected AI data centers. Multi-band optical networks have become the backbone of such connectivity. To accommodate increasingly dynamic traffic demands, these networks require continual resource re-optimization to improve resource utilization and reduce service blocking. However, multi-band transmission amplifies inter-channel stimulated Raman scattering(ISRS), inducing nonlinear coupling among services. This coupling complicates re-optimization, as local adjustments perturb the global power profile, creating interdependent optimization decisions across services. Moreover, the nonlinear interactions vary with the subset of services selected for re-optimization, and inappropriate selection may reduce re-optimization effectiveness or diminish achievable benefits. To address these challenges, we propose a Stackelberg Game with Multi-Agent Transformer (SG-MAT) approach for ISRS-aware resource re-optimization. SG-MAT employs a leader-follower structure, in which the MAT-based follower models nonlinear coupling and generates coordinated channel reallocations, feeding the utilization improvements to the leader. Using this feedback, the leader identifies critical service subsets, approximating a Stackelberg equilibrium to guide global decisions and enabling consistent and efficient re-optimization. Simulation results show that SG-MAT achieves the highest utilization improvement and reduces the blocking ratio by approximately 30% under moderate traffic loads, with computational complexity comparable to benchmarks.