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

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.

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Publication Details

Journal
Journal of AI-powered medical innovations.
Published
2024-12-25
DOI
https://doi.org/10.60087/japmi.vol.03.issue.01.id.003
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.35
Controls
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article

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

Sandeep Pochu, Sai Rama Krishna Nersu, Srikanth Reddy Kathram
1 citations
Journal of AI-powered medical innovations.
Anomaly Detection Techniques and Applications
0.35
article

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

Sandeep Pochu, Sai Rama Krishna Nersu, Srikanth Reddy Kathram
article en
1 citations

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.

Journal of AI-powered medical innovations.Vol. 3(1)
Synopsys (Switzerland) (CH)
Openalex Percentile: Top 30%
Anomaly Detection Techniques and Applications
0.35
DOIOpen Access PDFOpenAlex
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Scaling Kubernetes Clusters with AI-Driven Observability for Improved Service Reliability — Sandeep Pochu, Sai Rama Krishna Nersu, et al. · Journal of AI-powered medical innovations. (2024) | TGRS Research Map | TGRS