AI-Assisted Frequency Resource Management in Multi-Tenant Optical Networks

Abstract

Multi-tenant optical networks are becoming a foundational infrastructure for cloud services, enterprise connectivity, research collaborations, and emerging low-latency applications. In such environments, frequency resources are no longer managed solely as an engineering constraint but as a governed, contested, and economically significant asset. This paper examines artificial intelligence assisted frequency resource management in multi-tenant optical networks from a system-level and socio-technical perspective. It argues that effective management requires more than improved spectrum assignment algorithms; it demands architectural choices, operational policies, fairness safeguards, accountability mechanisms, and sustainability considerations that operate together across optical, computational, and institutional layers. The paper first reviews elastic optical networking and multi-tenancy as structural conditions that create both flexibility and coordination problems. It then analyzes frequency resource management as a control problem shaped by tenant isolation, service-level expectations, and infrastructure economics. Subsequent sections explore machine learning for traffic prediction, routing and spectrum assignment, quality-of-transmission estimation, defragmentation, and failure management. The discussion emphasizes trade-offs among efficiency, robustness, fairness, and explainability. Governance is treated as a first-order design concern, including auditability, contestability, and equitable allocation under scarcity. Deployment and sustainability are examined through energy use, lifecycle impacts, and the risk that AI optimization may shift rather than eliminate resource burdens. The paper concludes that AI-assisted frequency management should be embedded in accountable institutional frameworks rather than treated as a purely technical substitute for policy judgment.