AIOps: Transforming Management of Large-Scale Distributed Systems

AIOps (Artificial Intelligence for IT Operations) is transforming how organizations manage increasingly complex distributed systems. As enterprises adopt cloud-native architectures and microservices at scale, traditional monitoring approaches have reached their limits, unable to handle the volume, velocity, and variety of operational data. AIOps addresses these challenges by integrating machine learning and advanced analytics into IT operations, enabling anomaly detection, predictive analytics, automated incident resolution, enhanced root cause analysis, and optimized capacity planning. The evolution from manual operations to AI-augmented approaches demonstrates significant improvements in system reliability, operational efficiency, and cost reduction. Despite compelling benefits, successful implementation requires overcoming challenges in data quality, model training, cultural adaptation, and drift management. Looking forward, AIOps will continue evolving towards deeper development-operations integration, sophisticated self-healing capabilities, and enhanced natural language interfaces - ultimately transforming how organizations deliver reliable digital services in increasingly complex environments.

Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-03-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n5117
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

AIOps: Transforming Management of Large-Scale Distributed Systems

European Journal of Computer Science and Information Technology
Business Process Modeling and Analysis
article

AIOps: Transforming Management of Large-Scale Distributed Systems

article en

Abstract

AIOps (Artificial Intelligence for IT Operations) is transforming how organizations manage increasingly complex distributed systems. As enterprises adopt cloud-native architectures and microservices at scale, traditional monitoring approaches have reached their limits, unable to handle the volume, velocity, and variety of operational data. AIOps addresses these challenges by integrating machine learning and advanced analytics into IT operations, enabling anomaly detection, predictive analytics, automated incident resolution, enhanced root cause analysis, and optimized capacity planning. The evolution from manual operations to AI-augmented approaches demonstrates significant improvements in system reliability, operational efficiency, and cost reduction. Despite compelling benefits, successful implementation requires overcoming challenges in data quality, model training, cultural adaptation, and drift management. Looking forward, AIOps will continue evolving towards deeper development-operations integration, sophisticated self-healing capabilities, and enhanced natural language interfaces - ultimately transforming how organizations deliver reliable digital services in increasingly complex environments.

European Journal of Computer Science and Information TechnologyVol. 13(5)
Openalex Percentile: Top 89%
Business Process Modeling and Analysis
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