Authors: Keshav M. Rana
Abstract: Cloud-native observability platforms have revolutionized how organizations understand, measure, and improve service performance in distributed computing environments. Unlike traditional monitoring tools that focus on static metrics, observability provides a holistic, data-driven view of system behavior through the collection and correlation of metrics, logs, and traces. In dynamic environments powered by containers, microservices, and Kubernetes orchestration, such platforms enable real-time insights into performance bottlenecks, latency variations, and service dependencies. This comprehensive visibility helps teams identify root causes, optimize system efficiency, and enhance user experience. However, observability in cloud-native systems also introduces challenges, including data volume management, complex instrumentation, and high computational costs. Modern observability platforms address these issues through automation, AI-driven analytics, and scalable data architectures capable of handling multidimensional telemetry. This review explores the evolution, architecture, and influence of observability platforms on service performance visibility, highlighting their role in proactive fault detection, system resilience, and decision-making efficiency. It also examines the challenges and future directions shaping the next generation of observability frameworks that promise self-optimizing and predictive performance management in cloud-native ecosystems.
DOI: http://doi.org/10.5281/zenodo.17879382
