Analyzing the Influence of Processor Speed and Clock Speed on Remaining Useful Life Estimation of Software Systems

TL;DR

This paper investigates how environmental factors like processor and clock speed influence the estimation of remaining useful life in software systems, extending prior hardware-focused PHM research with validated experimental results.

Contribution

It introduces an extended analysis of environmental attributes affecting RUL estimation in software, validated through real data and statistical methods, filling a gap in software health management.

Findings

Processor and clock speed significantly impact RUL estimates.

Environmental attributes can alter software maintenance strategies.

Validated results support improved predictive models for software health.

Abstract

Prognostics and Health Management (PHM) is a discipline focused on predicting the point at which systems or components will cease to perform as intended, typically measured as Remaining Useful Life (RUL). RUL serves as a vital decision-making tool for contingency planning, guiding the timing and nature of system maintenance. Historically, PHM has primarily been applied to hardware systems, with its application to software only recently explored. In a recent study we introduced a methodology and demonstrated how changes in software can impact the RUL of software. However, in practical software development, real-time performance is also influenced by various environmental attributes, including operating systems, clock speed, processor performance, RAM, machine core count and others. This research extends the analysis to assess how changes in environmental attributes, such as operating system and clock speed, affect RUL estimation in software. Findings are rigorously validated using real performance data from controlled test beds and compared with predictive model-generated data. Statistical validation, including regression analysis, supports the credibility of the results. The controlled test bed environment replicates and validates faults from real applications, ensuring a standardized assessment platform. This exploration yields actionable knowledge for software maintenance and optimization strategies, addressing a significant gap in the field of software health management.