As next-generation networks begin to take shape, the necessity of Optical Transport Networks (OTNs) in helping achieve the performance requirements of future networks is evident. Future networks are characterized as being data-centric and are expected to have ubiquitous artificial intelligence integration and deployment. To this end, the efficient and timely transportation of fresh data from producer to consumer is critical. The work presented in this paper outlines the role of OTNs in future networking generations. Furthermore, key emerging OTN technologies are discussed. Additionally, the role intelligence will play in the Management and Orchestration (MANO) of next-generation OTNs is discussed. Moreover, a set of challenges and opportunities for innovation to guide the development of future OTNs is considered. Finally, a use case illustrating the impact of network dynamicity and demand uncertainty on OTN MANO decisions is presented.

No abstract

No abstract

No abstract

The advent of next-generation networks has revolutionized modern networking practices through its improved service capability as well as its numerous emerging use cases. Coupled with the increasing number of connected devices, 5G and beyond (5G+) network traffic is expected to be increasingly diverse and high in volume. To address the large amount of data exchanged between the 5G+ core and external data networks, optical transport networks (OTNs) with dense wavelength-division multiplexing (DWDM) will be leveraged. In order to prepare for this increase in traffic, network operators (NOs) must develop and expand their existing backbone networks, requiring significant levels of capital expenditures. To this end, the traffic grooming and infrastructure placement problem is critical to supporting NO decisions. The work presented in this paper considers the traffic grooming and infrastructure placement problem for OTN-over-DWDM networks. The dynamicity and diversity of 5G+ network traffic are addressed through the use of robust optimization, allowing for increasing levels of solution conservativeness to protect against various levels of demand uncertainty. Furthermore, a robust traffic grooming and infrastructure placement heuristic (RGIP-H) solution capable of addressing the scalability concerns of the optimization problem formulation is presented. The results presented in this work demonstrate how the tuning of the robust parameters affects the cost of the objective function. Additionally, the ability of the robust solution to protect the solution under demand uncertainty is highlighted when the robust and deterministic solutions are compared during parameter deviation trials. Finally, the performance of the RGIP-H is compared to the optimization models when applied to larger network sizes.