Enabling simulation services for digital twins of 5G/B5G mobile networks
Giovanni Nardini, Giovanni Stea
Abstract
Digital Twins (DTs) have been proposed as digital replicas of physical entities (e.g., manufacturing equipment), which one can observe in real-time and interact with. Digital Twins of Networks (DTNs) are increasingly being discussed in the literature, as an enabler for efficient data-driven network management and performance-driven network optimization (e.g., to support dynamic reconfiguration, or anticipate the effects of faults). A DTN includes service mapping models, i.e. models that can be fed with acquired data to produce insight on the network itself - e.g., to run what-if scenarios, based on multiple underlying technologies, from Machine Learning to analytical models, e.g. Markov Chains. In this paper we examine the case of DTNs of mobile networks, DTMNs, tailored to 5G and beyond, where issues of dynamic reconfiguration and fault anticipation are critical. We argue that simulation services should be offered by the DTMN in order to allow performance-driven network optimization, and that discrete-event network simulators are ideal instruments to be employed for this purpose. We discuss the challenges that need be addressed to make this happen, e.g., centralized vs. distributed implementation, gathering input from the physical network, security issues and hosting, and we review the possibilities offered by network simulation in terms of what-if analysis, defining the concepts of lockstep and branching analysis. We present a framework to endow a DTMN with simulation services and we exemplify it using Simu5G, a popular 5G/B5G simulation library for OMNeT++, as a reference case study.