A Unified Multi-Signal Correlation Architecture for Proactive Detection of Azure Cloud Platform Outages - Sai Bharath Sannareddy; Suresh Sunkari (The Eastasouth Journal of Information System and Computer Science, 2025)
Cloud platforms constitute the operational substrate for modern digital enterprises, yet their internal health telemetry remains intrinsically opaque, delayed, and non-deterministic from the perspective of tenant-facing reliability engineering. Despite the extensive instrumentation available within Microsoft Azure—including Service Health advisories, Resource Health telemetry, and platform diagnostic exports—empirical evidence continually demonstrates structural limitations that impede timely identification of regional instabilities, control-plane disruptions, propagation inconsistencies, and multi-service correlated failures. These limitations introduce latency between fault inception and observable acknowledgement, creating blind spots that severely constrain operational response windows for high-availability systems. This paper presents a novel Unified Multi-Signal Correlation Architecture (UMSCA) designed to overcome inherent deficiencies in provider-sourced telemetry by constructing a proactive, cross-signal, time-aligned reliability intelligence layer. The proposed framework integrates four heterogeneous data modalities—Azure Service Health, Azure Resource Health, Event Hub–streamed diagnostic telemetry, and distributed synthetic endpoint instrumentation—and fuses them using (i) canonical semantic normalization, (ii) probabilistic temporal alignment, (iii) inter-signal divergence detection, and (iv) multi-source reliability inference models. A large-scale enterprise simulation comprising 40 subscriptions, 18 geo-diverse Azure regions, 1,200 heterogeneous cloud resources, and over 3.2M telemetry events demonstrates that UMSCA reduces Mean Time to Detect (MTTD) by 88%, improves multi-signal correlation accuracy to 92%, lowers false-positive escalation by 52%, and estimates cross-region blast radius with up to 93% accuracy.