Abstract
An exponential growth of bandwidth demand, spurred by emerging network
services, often with diverse characteristics and stringent performance
requirements, drive the need for more dynamic operation of optical
networks, efficient use of spectral resources, and automation. Spectrum
fragmentation is one of the main challenges of dynamic,
resource-efficient Elastic Optical Networks (EONs). 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 Oldest-First
FirstFit (OF-FF) defragmentation heuristic, substantially reducing
blocking probability and defragmentation overhead.