  Digital Twin technology facilitates the monitoring and online analysis of
large-scale communication networks. Faster predictions of network performance
thus become imperative, especially for analysing Quality of Service (QoS)
parameters in large-scale city networks. Discrete Event Simulation (DES) is a
standard network analysis technology, and can be further optimised with
parallel and distributed execution for speedup, referred to as Parallel
Discrete Event Simulation (PDES). However, modelling detailed QoS mechanisms
such as DiffServ requires complex event handling for each network router, which
can involve excessive simulation events. In addition, current PDES for network
analysis mostly adopts conservative scheduling, which suffers from excessive
global synchronisation to avoid causality problems. The performance analysis of
optimistic PDES for real-world large-scale network topology and complex QoS
mechanisms is still inadequate. To address these gaps, this paper proposes a
simulation toolkit, Quaint, which leverages an optimistic PDES engine ROSS, for
detailed modelling of DiffServ-based networks. A novel event-handling model for
each network router is also proposed to significantly reduce the number of
events in complex QoS modelling. Quaint has been evaluated using a real-world
metropolitan-scale network topology with 5,000 routers/switches. Results show
that compared to the conventional simulator OMNeT++/INET, even the sequential
mode of Quaint can achieve a speedup of 53 times, and the distributed mode has
a speedup of 232 times. Scalability characterisation is conducted to portray
the efficiency of distributed execution, and the results indicate the future
direction for workload-aware model partitioning.
