NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks

Abstract

Network Digital Twin (NDT) and Network Simulation (NS) are two paradigms leveraging virtual representations of networks to help decision-making. These tools may seem similar or interchangeable and are often confused or opposed. However, they have their respective purposes, use cases and underlying concepts, which differ and are complementary. The goal of this article is to explore and clarify the specificity, the benefits and the limits of these two decision support tools, analyze how they complement each other and can be nicely combined. We argue that a smart integration of NS in NDT, named NS+NDT, can ease, accelerate and strength decisions for network design, deployment, operations, management and evolution. To study and demonstrate this claim, we focus on the domain of Internet of Things (IoT) solutions, where wireless networks are critical for connecting the physical assets to the Internet, but are complicated to configure for meeting the requirements of a specific application. We examine how NS, coupled with NDT, can contribute to support IoT architects and operators decisions throughout the life cycle of an IoT network. We analyze the different steps required to use NS in the context of NDT and examine how this helps remove NS barriers such as credibility and reliability. In particular, we show how NDT data enable to fine tune and customize the energy consumption models, making the simulation results more context-aware and insightful. Then, addressing the often-prohibitive simulation cost for exploring a large parameter space, we propose to associate surrogate modeling to NS+NDT. As surrogate models, we first introduce a simple ML (Machine Learning)-based surrogate model and illustrate this method with two IoT network configuration optimization use cases. Secondly, we propose a Bayesian optimization approach based on Gaussian Processes as surrogate model to further accelerate the (re)configuration decisions. We show how this method enables to select simulation scenarios that converge rapidly to the optimal solution, and allows the NDT to timely perform the adaptation. The contribution of this article is threefold. It provides (i) the first systematic analysis of the differences and potential synergies between NDT and NS; (ii) a synthetic presentation of the integration of NS and associated decision algorithms within a NDT to unlock NS accessibility and credibility throughout the life cycle of an IoT solution; (iii) a proposal for a smart and cost-efficient integration of NS in NDT via surrogate modeling, for reducing evaluation and optimization cost, paving the way to NS-augmented NDT-based dynamic adaptation and real-time optimization of IoT networks.