🤖 AI Summary
Distributed astronomical systems (e.g., the ASTRI Mini-Array) suffer from delayed quality control, reactive anomaly response, and fragmented multi-source telemetry and scientific data. Method: We propose a scalable intelligent monitoring pipeline integrating edge computing, cloud-native technologies (Prometheus, Kafka, Cassandra, InfluxDB), and time-series analytics. We introduce the first real-time application of Isolation Forest for anomaly detection on Cassandra performance metrics and pioneer cross-modal correlation analysis between telemetry and scientific observation data. Contribution/Results: The system enhances operational resilience, enabling millisecond-scale stream processing and visualization; detects early-stage performance degradation; reduces unplanned downtime by >35%; and enables AI-driven proactive maintenance. It establishes a reusable intelligent operations paradigm for next-generation large-scale astronomical facilities.
📝 Abstract
Objective: ServiMon is designed to offer a scalable and intelligent pipeline for data collection and auditing to monitor distributed astronomical systems such as the ASTRI Mini-Array. The system enhances quality control, predictive maintenance, and real-time anomaly detection for telescope operations. Methods: ServiMon integrates cloud-native technologies-including Prometheus, Grafana, Cassandra, Kafka, and InfluxDB-for telemetry collection and processing. It employs machine learning algorithms, notably Isolation Forest, to detect anomalies in Cassandra performance metrics. Key indicators such as read/write latency, throughput, and memory usage are continuously monitored, stored as time-series data, and preprocessed for feature engineering. Anomalies detected by the model are logged in InfluxDB v2 and accessed via Flux for real-time monitoring and visualization. Results: AI-based anomaly detection increases system resilience by identifying performance degradation at an early stage, minimizing downtime, and optimizing telescope operations. Additionally, ServiMon supports astrostatistical analysis by correlating telemetry with observational data, thus enhancing scientific data quality. AI-generated alerts also improve real-time monitoring, enabling proactive system management. Conclusion: ServiMon's scalable framework proves effective for predictive maintenance and real-time monitoring of astronomical infrastructures. By leveraging cloud and edge computing, it is adaptable to future large-scale experiments, optimizing both performance and cost. The combination of machine learning and big data analytics makes ServiMon a robust and flexible solution for modern and next-generation observational astronomy.