The Role of Predictive Analytics in Modern SRE Practices: A Path to Self-Healing Systems

Madhu Sudhan Nanda

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 3345-3354

Abstract: The integration of predictive analytics into Site Reliability Engineering (SRE) practices represents a transformative shift in managing complex digital systems. This article examines how predictive analytics is revolutionizing traditional SRE approaches, enabling organizations to transition from reactive incident management to proactive system maintenance. The article explores the evolution of SRE practices, analyzing how machine learning and statistical models enhance pattern recognition, automate response mechanisms, and optimize resource allocation. The article demonstrates that predictive analytics not only improves technical performance but also delivers significant environmental, economic, and societal benefits. Through comprehensive analysis of implementation challenges and best practices, the article provides insights into successful adoption strategies. The article indicates that organizations implementing predictive analytics in SRE achieve substantial improvements in service reliability, cost efficiency, and operational effectiveness, paving the way for truly self-healing systems.

Keywords: Site Reliability Engineering (SRE); Predictive Analytics; Self-Healing Systems; Machine Learning Operations; Infrastructure Automation (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112350
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT251112350 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT251112350/CSEIT251112350 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:1012

DOI: 10.32628/CSEIT251112350

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().