Abstract
Data Plane Programmability is redesigning the way we manage and operate data forwarding devices. Recent efforts led by the industry and academia have shown a growing adoption of intelligent and autonomous mechanisms to deal with an increasing number of problems. Despite all the progress made in the area, the application of Artificial Intelligence (AI) in network infrastructures is not that simple. One of the fundamental questions behind the adoption of AI in critical systems consists of determining whether the models have been designed and trained correctly and faithfully to reality. An emerging technology to verify and validate AI models consists of using Digital Twins (DT). DTs allow to create digital representations of physical entities in the network infrastructure. These digital representations are used as realistic prototypes to infer, for example, the correctness and performance of existing autonomous mechanisms or for simple future inferences. Despite reducing the risks associated with deploying and operating AI-based mechanisms in network infrastructures, operating DT-assisted network infrastructures requires substantial efforts in orchestrating resources to keep up with the requirements of physical and digital entities. In this project, we propose DigiNet, the first effort to implement, operate, and orchestrate Digital Twins-assisted network infrastructures. The project unfolds into three objectives: (i) to design a framework for allowing the correct description Digital Twin Networks models; (ii) to optimize the management and operation of Digital Twin Networks on programmable infrastructures in an analytical and experimental way; and (iii) to identify problems in the networking domain that can benefit from the use of Digital Twin Networks. The results to be obtained in this project have the potential to cause a lasting impact within its research area in the coming years. (AU)
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