LalithSriram Datla, Cloud Engineer, USA

Maintaining dependability, performance, and security in modern cloud environments-characterised by scattered, dynamic, and extensively monitored services-requires timely anomaly detection. Many times, conventional monitoring systems generate an excessive amount of alerts—many of which are repeated or delayed—which causes alert fatigue and missed events. This work presents a signal-based approach for anomaly detection using the interaction of log data and system measurements. We generate a multi-dimensional view of system activity by extracting ordered signals from unstructured logs via Log Insights and aggregating them with real-time data collected by Prometheus. The approach detects subtle trends and anomalies that can indicate breakdowns or disruptions, hence surpassing threshold-based monitoring. This method distinguishes itself by combining the quantitative depth of Prometheus measurements with high-fidelity log signals, therefore enabling a more contextually aware and proactive detection system. While measurements provide consistent, time-series performance indicators, logs offer comprehensive contextual narratives. Together, they help to cross-validate anomalies and reduce false positives. Our system continuously gathers and analyses data streams using statistical methods based on rules to find abnormalities as they develop. Through the connection of reactive alerting with predictive knowledge, this hybrid monitoring system enhances observability. Moreover, it helps teams in cloud operations to see problems early, understand their main causes faster, and, if at all possible, automate solutions. We show by practical case studies and performance benchmarks that the integration of Log Insights with Prometheus metrics improves the accuracy, timeliness, and applicability of anomaly detection. The result is a strong but simplified operational intelligence layer that improves system resilience and reduces downtime in systems built on clouds. This paper describes our approach's design, implementation, and results, therefore supporting a shift to signal-based, integrated observability in cloud operations.

Cloud Operations, Anomaly Detection, Log Insights, Prometheus Metrics, Signal Processing, Distributed Tracing, Service Mesh, Telemetry Data, Root Cause Analysis, Incident Management, Time Series Analysis, OpenTelemetry, System Monitoring, Alerting Rules, Resource Utilization