Cloud-edge acceleration strategies for discrete event simulation in manufacturing systems

Abstract

Industrial manufacturing simulations are constructive tools for emulating and stress-testing real workflows such that bottlenecks decrease when implementing or modifying the physical systems they represent. They are beneficial at generating unforeseen information, drawing conclusions from stochastic events, and are enablers of the smart factory paradigm. Computing industrial manufacturing simulation, however, particularly at scale, can be time-consuming. Owing to contemporary real-time processing advancements whereby simulations are becoming increasingly intertwined with their subjects, combined with the competitiveness of enterprises whereby instant data output is in demand from clients, growth concerning simulation acceleration and sustainability is widely coveted. High Performance Computing is central to this, encompassing strategies such as Multicore Processing and Artificial Intelligence. In light of sustainability, this thesis examines GPU and as-a-service strategies to accelerate the dominant category of manufacturing simulation, Discrete Event Simulation, in heterogeneous cloud-edge systems.Initially, cloud-based GPU computing was applied to accelerate three manufacturing factory case simulations of increasing complexity: source-sink, assembly-line balancing and reinforcement learning supply strategy. The same simulations, at equivalent computational scales and complexities, were subsequently analysed using edge-based GPU computing - consisting of two modes of execution: low-resourced and high-resourced. Speedups spanned between 1.2x and 3.2x, alongside the novel identification and quantification of the non-proportional relationship between computational scale and speedup based on high-level GPU acceleration in the cloud-edge spectrum. Stemming from this was a time, energy and cost analysis to discern how each metric was intertwined during DES execution. For the first time in the context of industrial manufacturing - the trade-offs between DES speed, energy and cost in the cloud-edge were described, the offset between each cloud-edge domain for each metric was measured, and the optimum cloud-edge domain was identified. The culmination of these contributions was the implementation of DES-as-a-service. This incorporated workload sharing between cloud and edge, and demonstrated an effective approach to scale up, offload and accelerate simulation - achieving a speedup high of 7.3x.

Date of Award	Jul 2025
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Northern Ireland Department for the Economy
Supervisor	Vishal Sharma (Supervisor), Adrian Murphy (Supervisor) & Carlos Reano (Supervisor)