EconPapers    
Economics at your fingertips  
 

Scaling Kubernetes Clusters with AI-Driven Observability for Improved Service Reliability

Sandeep Pochu (), Sai Rama Krishna Nersu () and Srikanth Reddy Kathram ()

Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 2024, vol. 3, issue 1, 39-52

Abstract: This study introduces an AI-powered observability framework integrated with Kubernetes clusters using Prometheus and Grafana. It demonstrates how predictive analytics reduces mean time to resolution (MTTR) and optimizes resource allocation. The research outlines a case study with measurable gains in service reliability and cost-effectiveness.

Keywords: Kubernetes Scaling; AI-Driven Observability; Service Reliability; Cluster Management; Kubernetes Optimization; AI-Powered Monitoring; Scalability in Kubernetes; Observability Tools; Automated Cluster Scaling; Service Uptime; Fault Tolerance (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://japmi.org/index.php/japmi/article/view/19/17 (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:abu:abuabu:v:3:y:2024:i:1:p:39-52:id:19

Access Statistics for this article

More articles in Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930) from Open Knowledge
Bibliographic data for series maintained by By Openjournaltheme ().

 
Page updated 2025-04-10
Handle: RePEc:abu:abuabu:v:3:y:2024:i:1:p:39-52:id:19