No abstract

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.

Online quality of transmission (QoT) monitoring and validation enables conversion of unused margins into higher network capacities. We quantify the benefit of long-term performance awareness in a Pan-European optical network of a Tier-1 operator.

One of the main obstacles to efficient resource usage under dynamic traffic in elastic optical networks (EONs) is spectrum fragmentation (SF), leading to blocking of incoming service requests. Proactive spectrum defragmentation (SD) approaches periodically reallocate services to ensure better alignment of available spectrum slots across different links and alleviate blocking. The services for reallocation are commonly selected based on their properties, e.g., age, without detailed consideration of prior or posterior spectrum occupancy states. In this paper, we propose a heuristic algorithm for proactive SD that considers different spectrum fragmentation metrics to select services for reallocation. We analyze the relationship between these metrics and the resulting service blocking probability. Simulation results show that the proposed heuristic outperforms the benchmarking proactive SD algorithms from the literature in reducing blocking probability.

We present and demonstrate service provisioning in partially disaggregated multivendor network automation scenarios with online physical impairment validation. This work uses and extends standard interfaces (OpenConfig and ONF Transport API) to retrieve network information interacting with TIP GNPy tool.