Performance Analysis of a Data-Driven QoT Decision Approach on a Dynamic Multicast-Capable Metro Optical Network
| Main Authors: | Tania Panayiotou, Sotirios P. Chatzis, Georgios Ellinas |
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| Format: | Article Journal |
| Terbitan: |
, 2017
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1036413 |
Daftar Isi:
- The performance of a data-driven quality-of-transmission (QoT) model is investigated on a dynamic metro optical network capable of supporting both unicast and multicast connections. The data-driven QoT technique analyzes data of previous connection requests and, through a training procedure that is performed on a neural network, returns a data-driven QoT model that nearaccurately decides the QoT of the newly arriving requests. The advantages of the data-driven QoT approach over the existing Q-factor techniques are that it is self-adaptive, it is a function of data that are independent from the physical layer impairments (PLIs) eliminating the requirement of specific measurement equipment, and it does not assume the existence of a system with extensive processingandstorage capabilities. Further, it is fast in processing new data and fast in finding a near-accurateQoT model provided that such a model exists. On the contrary, existing Q-factor models lack self-adaptiveness; they are a function of the PLIs, and their evaluation requires time-consuming simulations, lab experiments, specific measurement equipment, and considerable human effort. It is shown that the data-driven QoT model exhibits a high accuracy (close to 92%–95%) in determining, during the provisioning phase, whether a connection to be established has a sufficient (or insufficient) QoT, when compared with the QoT decisions performed by the Q-factor model. It is also shown that, when sufficient wavelength capacity is available in the network, the network performance is not significantly affected when the data-driven QoT model is used for the dynamic system instead of the Q-factor model, which is an indicator that the proposed approach can efficiently replace the existing Q-factor model.
- 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. T. Panayiotou, S. P. Chatzis and G. Ellinas, "Performance analysis of a data-driven quality-of-transmission decision approach on a dynamic multicast- capable metro optical network," in IEEE/OSA Journal of Optical Communications and Networking, vol. 9, no. 1, pp. 98-108, Jan. 2017. doi: 10.1364/JOCN.9.000098