Telemetry Quality Indexing for AI-Ready Observability in Multi-Node Cloud Systems: A Multi-Dimensional Framework for Distributed Infrastructure Diagnosis
Sneha Gullapalli, Veera Ravindra Divi · International Journal of Engineering Development and Research · 2026
AI-driven observability platforms rely on telemetry signals such as metrics, logs, traces, events, and resource metadata to detect anomalies, diagnose failures, and support operational decision-making in distributed cloud environments. However, the effectiveness of these systems is often limited by telemetry-quality issues, including missing attributes, inconsistent semantic labeling, delayed ingestion, broken trace context, excessive cardinality, and poor diagnostic relevance. This paper presents the Telemetry Quality Index (TQI), a multi-dimensional framework for assessing the AI-readiness of observability data in multi-node cloud systems. TQI evaluates telemetry across six dimensions: signal completeness, semantic conformance, freshness, correlation readiness, cardinality safety, and diagnostic utility. A reproducible synthetic simulator comprising 50 seeds, 100 service instances per seed, and six injected defect classes is used for evaluation. Experimental results show that TQI outperforms volume-only, completeness-only, semantic-only, and instrumentation-score baselines in identifying weak telemetry while providing actionable remediation insights. The framework serves as an input-readiness layer for AI-assisted diagnosis and self-healing infrastructures.