International Journal of Science, Engineering and TechnologyAuthors: Sriram Ghanta
Abstract: Distributed Java microservices have become foundational to modern enterprise systems, yet their operational complexity has made root cause discovery a persistent challenge in production environments. As service interactions grow deeper and failure pathways become increasingly nonlinear, traditional diagnostic methods struggle to isolate underlying causes with sufficient speed or accuracy. This study examines the systemic behavior of failure propagation within Java based microservices and proposes an intelligent, system level approach for uncovering root causes across interconnected runtime layers. Using a mixed methodological design that combines architectural analysis, qualitative examination of failure scenarios, and quantitative evaluation of diagnostic signal patterns, the research maps how logs, traces, state transitions, and resource pressures interact to reveal hidden causal structures. The analysis demonstrates that meaningful root cause discovery emerges from correlating multi source observability data with runtime behavior models that reflect service dependencies, temporal alignment, and interaction context. Findings indicate that intelligent correlation logic, when embedded within a system aware diagnostic framework, can significantly reduce investigation time and improve the precision of fault localization. The study contributes to academic and industry discussions by establishing a structured conceptual foundation for intelligent root cause discovery at a time when distributed systems continue to expand in scale and complexity. The implications highlight how diagnostic intelligence can strengthen operational resilience, guide architectural decisions, and support more dependable service ecosystems across diverse Java based microservice deployments.
DOI: http://doi.org/10.5281/zenodo.17760543
