Abstract
Increasing demand in the backbone Dense Wavelength Division (DWDM) Multiplexing network traffic prompts an introduction of new solutions that allow increasing the transmission speed without significant increase of the service cost. In order to achieve this objective simpler and faster, DWDM network reconfiguration procedures are needed. A key problem that is intrinsically related to network reconfiguration is that of the quality of transmission assessment. Thus, in this contribution a Machine Learning (ML) based method for an assessment of the quality of transmission is proposed. The proposed ML methods use a database, which was created only on the basis of information that is available to a DWDM network operator via the DWDM network control plane. Several types of ML classifiers are proposed and their performance is tested and compared for two real DWDM network topologies. The results obtained are promising and motivate further research.
Highlights
The demand for network bandwidth is constantly growing due to emerging Internet applications such as high-definition video streaming, cloud, 5G, internet of things (IoT), virtual reality, etc
It is interesting to notice that for dataset 1 the random forest algorithm was able to successfully use the biggest attribute subset 4, the Support Vector Machines (SVM) and xgboost worked best with the medium subset 2, whereas logistic regression and decision trees achieved their best performance with the smallest subset 0
When looking at ROC curves the level of predictive performance might appear very good or near perfect, with little differences between algorithms, all of which appear fully capable of delivering predictions with high true positive rates and low fall positive rates
Summary
The demand for network bandwidth is constantly growing due to emerging Internet applications such as high-definition video streaming, cloud, 5G, internet of things (IoT), virtual reality, etc. The special circumstances related to Covid-19 prompted many services to move online, further increasing the demand for internet access with high bandwidth and quality of service (QoS). The backbone network must rely on high bandwidth optical links and on a very good transmission quality. In order to improve the commercial viability, elastic optical networks (EONs) have been proposed to use physical layer resources intelligently and efficiently to increase backbone network capacity and enable dynamic services [2,3,4,5,6,7,8]. Commercial optical network equipment providers offer coherent Dense Wavelength Division Multiplexing (DWDM) technology capable of establishing optical channels with 100 Gigabit per second (Gbps), 200 Gbps, 400 Gbps, and even 1 Tbps throughput [9,10,11,12]
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