Distributed Observability Strategies for Enterprise Systems: Integrating Metrics, Logs, Traces, and Intelligent Diagnosis

Shekar Vollem

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 6, 757-769

Abstract: Enterprise applications have evolved from monolithic architectures into highly distributed environments consisting of microservices, containers, cloud platforms, databases, message brokers, APIs, and dynamically provisioned infrastructure, enabling greater scalability, flexibility, and resilience while introducing substantial operational complexity. As transactions increasingly traverse numerous services and infrastructure layers, traditional monitoring based primarily on infrastructure metrics, predefined alerts, and isolated log inspection often fails to provide sufficient context for identifying performance degradation, cascading failures, and their underlying causes. Distributed observability addresses these limitations by collecting, correlating, and analyzing metrics, logs, and distributed traces to create a comprehensive view of system behavior across application and infrastructure boundaries. Early studies such as Pinpoint, Magpie, and X-Trace established important foundations for distributed problem determination and request tracing, while Dapper demonstrated the feasibility of production-scale distributed tracing and subsequent research advanced scalable log parsing, machine-learning-based anomaly detection, and automated trace analysis. This article reviews the evolution of distributed observability strategies for enterprise systems and proposes an integrated approach combining telemetry collection, cross-service correlation, intelligent anomaly detection, root-cause diagnosis, performance analysis, and operational response to improve reliability and provide actionable insights in complex distributed environments.

Keywords: Distributed Observability; Enterprise Systems; Distributed Tracing; Microservices; Telemetry; Metrics; Logs; Anomaly Detection; Root Cause Analysis; Cloud Computing; Site Reliability Engineering; Performance Monitoring; Reliability Engineering (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113403
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT25113403 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT25113403/CSEIT25113403 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i6:id:2145

DOI: 10.32628/CSEIT25113403

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().