Citation :

Saranya Monoharan, 2026. "AI-Assisted Observability and Proactive Failure Detection in Large-Scale Distributed Software Systems" ESP International Journal of Science, Humanities & Management Studies(ESP-IJSHMS)  Volume 4, Issue 4: 16-29.

Abstract :

Large-scale distributed software systems increasingly rely on microservices, containers, orchestration platforms, distributed databases, application programming interfaces, and cloud infrastructure to deliver scalable and highly available digital services. However, their dynamic topology and complex service dependencies make conventional threshold-based monitoring insufficient for detecting subtle degradation patterns and emerging failures before they affect users. This paper presents an AI-assisted observability framework for proactive failure detection and reliability management in large-scale distributed software systems. The proposed framework integrates heterogeneous telemetry sources, including system metrics, application logs, distributed traces, infrastructure events, deployment events, and service-dependency information, to construct a context-aware representation of system behavior. Machine-learning techniques are applied to establish dynamic operational baselines, perform multivariate anomaly detection, model temporal dependencies, forecast resource and performance degradation, and estimate evolving failure risk. Service dependency graphs and temporal reasoning are further incorporated to correlate anomalies across interconnected components, distinguish originating failures from propagated symptoms, and support explainable root-cause localization. The framework also introduces adaptive learning mechanisms to address concept drift resulting from workload variations, software releases, autoscaling, infrastructure modifications, and changing service relationships. A confidence-aware decision layer transforms predictive insights into operational responses while maintaining human oversight for high impact remediation actions. Evaluation is structured around precision, recall, F1-score, false-positive rate, detection lead time, root-cause localization accuracy, incident-correlation accuracy, and analysis latency. By combining multimodal telemetry correlation, predictive analytics, dependency-aware reasoning, explainable diagnosis, and continuous model adaptation, the proposed approach shifts observability from reactive incident monitoring toward proactive and adaptive reliability management, enabling earlier failure detection and more efficient operational decision-making in complex distributed software environments.

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Keywords :

AI-Assisted Observability, Distributed Software Systems, Proactive Failure Detection, Multimodal Telemetry, Anomaly Detection, Machine Learning, Temporal Forecasting, Root-Cause Analysis, Service-Dependency Graphs, Failure Prediction, Adaptive Learning, Explainable AI, Reliability Engineering, Incident Correlation, Predictive Analytics