Glasgow Scientists Build 25,000 Times Faster Way of Testing Computer Networks

A team of researchers at the University of Glasgow (UG) in Scotland have developed a new Machine Learning tool that they say could “transform network testing” by harnessing digital twins (i.e. digital replicas of physical systems or processes), making the process 25,000 times faster than traditional approaches.

Shenjia Ding, a research student in Glasgow’s School of Computing Science, used automatically-generated digital twins – built with Automated Machine Learning (AutoML) that can be used by non-experts with limited machine learning expertise – to test two complex American and European computer networks with 12 and 37 nodes (data receiving and processing points) respectively.

The test included six different types of traffic, including web browsing, video streaming and file downloads, alongside continuous congestion and background noise to simulate real conditions. The team’s digital twin took just 4.78 seconds to accurately test the speed of the networks, while a traditional simulator used in the test took 33 hours.

Clearly, we’re talking about much deeper testing than a quick consumer broadband speedtest, one that seems to be aimed at larger and thus much more complex computer networks.

Ms Ding said:

“Our results show that testing computer networks with automatically-generated digital twins can achieve high accuracy and significantly faster speeds than traditional simulator-based testing. We’re demonstrating a very promising alternative to manual and time-consuming testing that also relies heavily on professional expertise.”

Paul Harvey, co-author of the research and Senior Lecturer, said:

“Transport, like computing, is seeing enormous growth in data volumes, and in both instances, the pressure on the communications networks carrying all this data is immense.

By proving that we can use machine learning to build digital twins – which is another time-consuming and laborious task – we are highlighting the huge potential of this research to also test and optimise transport – and other networks that we rely on daily.”

Dr Harvey is a Co-Investigator for TransiT, a national research hub using digital twins and associated technologies to identify the fastest, least-risky and lowest cost pathways to transport decarbonisation in the UK. He said Ms Ding’s research could potentially support TransiT, particularly its goal of creating a ‘digital twin factory’ that can automate the production of digital twins for transport settings.

The researchers say their future work will focus on validating the digital twin’s update mechanisms and cost, assessing performance in real-time network environments, and conducting a comparative study across diverse network scenarios.

Ms Ding will present a paper on her work – ‘Automated Digital Twin Generation for Network Testing: A Multi-Topology Validation‘ – this month in Glasgow at the 2026 IEEE International Conference on Communications (ICC). The co-authors of the research are Paul Harvey and David Flynn at University of Glasgow.