Diagnostic analysis and performance optimization of scalable computing systems in the context of industry 4.0

Abstract

Escalating energy costs and sustainability concerns in high-performance computing (HPC) and industrial-scale systems demand advanced models for energy-efficient operations. Traditional discrete event system (DES) models, while valuable tools, often struggle with the complexities of real-world systems, particularly when dealing with simultaneous events, partial sequences, and false positives. To address these limitations, this paper introduces V-nets, a novel formalism that offers a more robust approach to modeling and analyzing complex event sequences. V-nets excel at handling concurrent events, incorporating temporal constraints, and accurately detecting partial sequences, leading to improved system diagnostics and energy efficiency. By leveraging V-nets, we can gain deeper insights into the behavior of complex systems, identify potential bottlenecks, and optimize resource allocation. This can lead to significant energy savings and improved system performance. For example, in HPC systems, V-nets can be used to monitor the energy consumption of individual components, identify idle resources, and optimize workload scheduling. In industrial settings, V-nets can help detect anomalies in production processes, leading to timely interventions and reduced downtime. The potential applications of V-nets are vast, extending beyond HPC systems to various industrial domains. As AI-driven workloads continue to grow in complexity, V-nets can play a crucial role in monitoring and optimizing energy consumption in these systems. By bridging the gap between theoretical advancements and real-world applications, V-nets have the potential to revolutionize the field of DES modeling and contribute to the development of more sustainable and efficient systems.