Cloud-Native Observability for Distributed Enterprise Systems Using OpenTelemetry and Predictive Analytics for Operational Reliability
DOI:
https://doi.org/10.21590/Keywords:
Cloud-native observability, OpenTelemetry, predictive analytics, distributed systems, operational reliability, anomaly detection, microservices monitoring, machine learning, telemetry correlation, root cause analysisAbstract
Cloud-native enterprise systems increasingly rely on distributed microservices, containerized applications, Kubernetes orchestration, and multi-cloud infrastructure to support business-critical operations. However, their dynamic architectures introduce challenges in monitoring application behavior, identifying performance bottlenecks, correlating failures, and maintaining operational reliability. Traditional monitoring approaches often depend on isolated metrics, logs, and traces, limiting their ability to identify complex dependencies and anticipate emerging incidents. This research proposes a cloud-native observability framework integrating OpenTelemetry with predictive analytics to improve reliability across distributed enterprise systems. The framework collects standardized telemetry data, including metrics, logs, and distributed traces, through OpenTelemetry instrumentation and exporters. A centralized processing pipeline normalizes, enriches, correlates, and securely transfers telemetry information to an analytical platform. Machine learning models analyze historical and real-time observations to detect anomalies, predict service degradation, estimate incident risks, and identify potential resource bottlenecks. The proposed architecture further integrates intelligent alert prioritization, dependency-aware root cause analysis, and automated remediation recommendations to reduce operational disruption. Evaluation is designed around operational reliability indicators, including anomaly detection accuracy, prediction lead time, mean time to detect, mean time to recover, and resource utilization. Comparative experiments against threshold-based monitoring establish a basis for assessing improvements. The framework aims to strengthen proactive incident management, improve service availability, optimize infrastructure utilization, and support scalable observability practices across heterogeneous enterprise environments while preserving telemetry privacy and minimizing monitoring overhead.
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