Network monitoring and observability have become essential components in managing modern cloud systems, where distributed architectures, dynamic workloads, and microservices-based applications introduce significant complexity. This review provides a comprehensive analysis of traditional network monitoring techniques and the evolution toward full-stack observability in cloud environments. It examines key concepts such as metrics, logs, and traces, which collectively enable deep visibility into system behavior, performance, and reliability. The study explores cloud-native monitoring tools and frameworks, including Prometheus, Grafana, OpenTelemetry, and distributed tracing systems, highlighting their roles in detecting anomalies, diagnosing issues, and ensuring service availability. Additionally, the integration of artificial intelligence and machine learning in observability platforms is discussed, emphasizing their ability to provide predictive insights and automate incident response through AIOps. Challenges such as data volume management, alert fatigue, latency, and interoperability are critically analyzed, along with best practices for designing scalable and efficient monitoring strategies. The review concludes that effective observability is crucial for maintaining performance, reliability, and user experience in cloud systems, enabling organizations to proactively manage complex distributed infrastructures.
Daniel Okeke (Tue,) studied this question.