Digital Twin-Based Optimization of Cloud-Oriented Optical Communication Networks
Keywords:
digital twin; optical communication networks; cloud computing; network optimization; sustainability; resilience; governance; fairnessAbstract
Cloud-oriented optical communication networks increasingly serve as critical infrastructure for distributed computation, storage, and latency-sensitive applications. Their operation requires coordinated decisions across optical transmission, switching, data-center interconnection, service orchestration, and energy management. Digital twins offer a promising system-level approach for these networks by maintaining synchronized virtual representations that can be used for monitoring, prediction, optimization, and policy testing. This paper examines the architectural, operational, and governance implications of digital twin-based optimization in cloud-oriented optical networks. It argues that the value of digital twins lies not merely in improved telemetry or simulation but in their capacity to mediate among competing objectives such as capacity, latency, resilience, sustainability, fairness, and accountability. The paper develops a reference architecture that connects physical optical elements, streaming telemetry, semantic models, predictive analytics, and closed-loop control. It then analyzes structural trade-offs in deployment, including centralized versus federated twins, public versus private governance, and vendor-specific versus standards-based interfaces. Particular attention is given to robustness under failure and disaster, equity in service provisioning, energy and lifecycle burdens, and policy frameworks for multi-tenant infrastructure. Through cross-domain comparisons with manufacturing, cloud computing, and power systems, the paper identifies conditions under which digital twins improve network performance without reproducing opacity, locking, or unfair resource allocation. It concludes that digital twin optimization should be treated as a socio-technical institution as much as a computational technique, requiring transparent models, accountable control, and inclusive governance.
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