Applying AI/ML to Kubernetes Logging and Monitoring in Enhancing Observability Through Intelligent Systems (Published)
As Kubernetes adoption accelerates in cloud-native architectures, ensuring robust observability across dynamic, large-scale clusters has become a critical operational challenge. Traditional logging and monitoring systems—relying heavily on rule-based alerting and manual log inspection—struggle to scale with the volume, velocity, and complexity of modern workloads. These approaches often lead to alert fatigue, delayed incident response, and incomplete root cause analysis.This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance observability within Kubernetes environments. By leveraging unsupervised learning for anomaly detection, natural language processing (NLP) for log parsing, and supervised models for event classification, the proposed intelligent observability framework significantly improves signal-to-noise ratios and accelerates troubleshooting processes. Through empirical evaluation on a production-grade Kubernetes testbed, the system demonstrated a 35% improvement in anomaly detection accuracy and reduced mean time to resolution (MTTR) by over 40% compared to baseline tools. These results highlight the transformative potential of AI/ML in enabling proactive, scalable, and context-aware monitoring solutions for complex cloud-native infrastructures.
Keywords: Artificial Intelligence, Logging, Monitoring, anomaly detection, kubernetes, machine learning, observability
Modernizing Legacy Systems: A Journey to Kubernetes-Based Microservices (Published)
The modernization of legacy systems through containerization and orchestration with Kubernetes represents a transformative approach to addressing limitations in traditional monolithic architectures. This comprehensive journey encompasses architectural redesign, technological upgrades, and operational transformation to meet current business demands for agility, scalability, and innovation. The transition from tightly coupled monoliths to loosely coupled microservices enables organizations to develop, deploy, and scale components independently while improving fault isolation and resource utilization. Kubernetes serves as a foundational platform for this transformation, providing declarative configuration, self-healing capabilities, and sophisticated traffic management that collectively address traditional limitations. The modernization process requires systematic assessment frameworks, strategic decision-making between incremental and complete transformation approaches, and implementation of essential patterns including Domain-Driven Design for service decomposition and Infrastructure-as-Code for operational automation. Organizations implementing these changes experience significant improvements across operational efficiency, development velocity, and business agility dimensions. Despite implementation challenges, the resulting architectural paradigm delivers substantial benefits including enhanced reliability, improved resource utilization, and accelerated innovation cycles that position organizations for sustained competitive advantage in rapidly evolving digital environments.
Keywords: Legacy modernization, Microservices architecture, cloud-native transformation, container orchestration, kubernetes