AI-DRIVEN OBSERVABILITY FOR HYPERSCALE COLOCATION  DATA CENTERS: A CASE STUDY OF LOG ANALYTICS AND  ANOMALY DETECTION PIPELINES

Manoj Kumar Kagitha

AI-DRIVEN OBSERVABILITY FOR HYPERSCALE COLOCATION DATA CENTERS: A CASE STUDY OF LOG ANALYTICS AND ANOMALY DETECTION PIPELINES

Authors

Manoj Kumar Kagitha

Keywords:

Data center observability, artificial intelligence, log analytics, anomaly detection, machine learning, hyperscale infrastructure, DevOps, AIOps

Abstract

The rapid proliferation of hyperscale colo data center collocations has presented unparalleledissues relating to infrastructure monitoring and operational intelligence. Traditional observability systems struggle to cope with the vast corpus of telemetry data that these
environments generate. This study sought to explore the possibility of employing AI-enabled observability solutions

References

Beyer, B., Jones, C., Petoff, J. and Murphy, N.R. (2018) The Site Reliability Workbook: Practical Ways to Implement SRE. Sebastopol: O'Reilly Media.

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Published

2019-11-20

How to Cite

Manoj Kumar Kagitha. (2019). AI-DRIVEN OBSERVABILITY FOR HYPERSCALE COLOCATION DATA CENTERS: A CASE STUDY OF LOG ANALYTICS AND ANOMALY DETECTION PIPELINES. Journal of Computational Analysis and Applications (JoCAAA), 27(7), 131–150. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5053