AUTHORS: Kamel Rahomoua, Ayman Ali

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ABSTRACT: The improvement in the conventional optical Transport network (OTN) is exceptionally moderate regarding the rapid growth in the mobile and the IP-Core technologies, particularly with the requirements of the new 5G advances. There are many challenges in the OTN such the expensive cost of the multilayer services planning, the quality of the services and the quality of the resilience must be recovered first to cope with the changes in the new generations of the access communication networks. The needs to overcome many of these challenges become vital nowadays, and depend on many factors in the OTN such the status of the optical fiber cables, the flexibility, the responsive and the availability of OTN assets to the direct customer control. In this paper for the first time a new proposed model is introduced by reorganizing the OTN to fit the needs of the new generations of the communications market, the model consolidates two promising technologies with each other which are the Software Defined Network (SDN) and the Machine Learning (ML) to overcome the previous challenges and to reconstruct the traditional OTN to be more smart, virtualized and automated. The fundamental role of the SDN is to transform the services on the OTN to be more dynamic rather fixed, at the same time the aim of the ML such the Artificial Neural Network (ANN) is to help the centralized controller of the SDN in the OTN by the past experience of the performance of the optical links in the OTN, this enables the centralized controller to formulate the right decisions about the optimized routes of the services restoration between the different domains and multilayers in the OTN. For the first time, the optical cloud concepts are introduced in the OTN by slicing and virtualizing the various domains with its vendors in the heterogeneous optical network to 3 integrated unified layers, this provides the required resiliencies and the bandwidth on demand between the multilayers and the different domains in the OTN in a more elastic way. The model is tested using 2 methods , the 1'st method is done by a software simulation by using a SPSS software model and its input data was 500 records from real OTN , and the 2'nd method was done by performing practical case study on the long distances heterogeneous OTN network in one of the middle east countries about the integrations between the different optical network domains, slicing the optical network, and the centralized controller to reconstruct the heterogonous OTN to 3 layers to perform the resilience of the services of the multi failure in the same domain through the multilayers in optical network. The results of the new model according to the practical case study in the long-distance heterogamous OTN show that: The dependence on the single vendor is nearly neglected with applying the concept of the clouding and slicing in the heterogeneous OTN, the pay for the end-users bandwidths has become possible and the time to provide the bandwidth on demand has become very short , the meshing between the heterogeneous optical network became available and the resilience for diamond services improved from 25% for double or triple faults to 100% after applying part of our model in the long distance optical network, the available bandwidth of the optical core network in the long distance network is optimized by more than 25% , the revenue from some OTN domains which have free bandwidths more than 50 % is increased by more than 50%, the switching time enhanced by about 50%, and the latency reduced from 27 msec to 742 usec for the selected routes which is optimized from the centralized controller.

KEYWORDS: Software Defined Network (SDN), Machine Learning (ML), Optical Transport Network (OTN), Dynamic Services, Network Mesh, Services Resilience, Heterogeneous Optical Network

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