DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation
Paper in proceeding, 2022

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.

Spectrum defragmentation

Service blocking ratio

Reinforcement learning

Author

Ehsan Etezadi

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Renzo Diaz

Telia

Anders Lindgren

Telia

Stefan Melin

Telia

Lena Wosinska

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Paolo Monti

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings

3694-3699
9781665435406 (ISBN)

IEEE Global Communications Conference
Rio de Janeiro, Brazil,

Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)

VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.

Subject Categories

Computer Engineering

Telecommunications

Communication Systems

Areas of Advance

Information and Communication Technology

DOI

10.1109/GLOBECOM48099.2022.10000736

More information

Latest update

10/26/2023