Design And Implementation Of Bug Resolution Prediction Scheme On Hpc Systems Through Large-Scale Log Analysis

Abstract

In the realm of High-Performance Computing (HPC), where computational power propels scientific advancement to new heights, the complexity and scale of modern systems bring forth a formidable challenge - the mitigation of software bugs and performance bottlenecks. Addressing this issue necessitates proactive and automated methodologies that enable bug prediction and resolution before their disruptive consequences emerge. To this end, we present an innovative Bug Resolution Prediction Scheme (BRPS), leveraging large-scale log analysis as the cornerstone of its predictive prowess. Our meticulously crafted scheme seeks to fortify HPC system reliability, minimise operational downtime, and optimise overall performance.

Delving into the depths of existing research, we perform a comprehensive review of bug prediction and resolution techniques, underscoring the need for advancements in automated analysis and detection. By meticulously collecting and pre-processing log data from HPC systems, we lay the foundation for precise feature extraction, identifying key indicators that influence bug occurrences. Drawing upon sophisticated machine learning algorithms and statistical techniques, our BRPS constructs a robust prediction model that exhibits unparalleled accuracy.

Embodied in a real-time implementation, our BRPS effortlessly integrates into existing monitoring and management frameworks, tirelessly vigilant in its quest to predict and preempt bugs. Through extensive experiments on a vast HPC cluster, we validate the efficacy of our scheme, demonstrating its potential to revolutionise the way HPC systems are managed and maintained. In conclusion, our Bug Resolution Prediction Scheme embodies the fusion of cutting-edge technology and meticulous data analysis, poised to elevate HPC system management to an unprecedented echelon of efficiency and resilience.