Efficient resource allocation and management can enhance the capacity of an optical backbone network. In this context, spectrum retuning via hitless defragmentation has been presented for elastic optical networks to enhance efficient spectrum accommodation while reducing the unused fragmented spaces in the spectrum. However, the quality of service committed in a service level agreement may be affected due to spectrum retuning. In particular, for transmission beyond the conventional C band, the presence of inter-channel stimulated Raman scattering can severely degrade the quality of the signal during defragmentation. To conquer this problem, this paper proposes, for the first time to our knowledge, a signal-quality-aware proactive defragmentation scheme for the
This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to
- Award ID(s):
- 1836921
- NSF-PAR ID:
- 10209466
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of Optical Communications and Networking
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 1943-0620; JOCNBB
- Page Range / eLocation ID:
- Article No. B1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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