APDT: A Digital Twin for Assessing Access Point Characteristics in a Network

TL;DR

This paper introduces APDT, a digital twin for computer networks that monitors, simulates, and predicts network performance issues like congestion, aiming to improve QoS through real-time data and predictive modeling.

Contribution

The paper presents a novel digital twin for networks that integrates real-time data collection, simulation, and forecasting to enhance network management and performance.

Findings

APDT successfully predicts short-term traffic surges.

APDT improves QoS by reducing traffic congestion.

The system enables proactive network management.

Abstract

Digital twins (DT) have emerged as a transformative technology, enabling real-time monitoring, simulations, and predictive maintenance across various domains, though their Application in the networking domain remains underexplored. This paper focuses on issues such as increasing client density and traffic congestion by proposing a digital twin for computer networks. Our Digital Twin, named Access Point Digital Twin (APDT) is used for tracking user behavior and changing bandwidth demands, directly impacting network performance and Quality of Service (QoS) parameters like latency, jitter, etc. APDT captures the real-time state of networks with data from access points (APs), enabling simulation-based analyses and predictive modelling. APDT facilitates the simulation of various what-if scenarios thereby providing a better understanding of various aspects of the network characteristics. We tested APDT on our University network. APDT uses data collected from three access points via the Ruckus SmartZone API and incorporates NS-3 based simulations. The simulation replicates a real-time snapshot from a Ruckus access point and models metrics such as latency and inter-packet transfer time. Additionally, a forecasting model predicts traffic congestion and suggests proactive client offloading, enhancing network management and performance optimization. Preliminary results indicate that APDT can successfully predict short-term traffic surges, leading to improved QoS and reduced traffic congestion.