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All Journal The Eastasouth Journal of Information System and Computer Science
Sannareddy, Sai Bharath
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Computer Science & IT

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A Unified Multi-Signal Correlation Architecture for Proactive Detection of Azure Cloud Platform Outages Sannareddy, Sai Bharath; Sunkari, Suresh
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.845

Abstract

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.
Ai-Driven Devops Automation for Ci/Cd Pipeline Optimization Kakarla, Roshan; Sannareddy, Sai Bharath
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.849

Abstract

Modern CI/CD pipelines have become cognitively overloaded, policy-fragile, and operationally inefficient as software delivery scales across microservices, multi-cloud platforms, and regulated environments. While DevOps automation has improved deployment velocity, it remains largely rule-based, reactive, and incapable of reasoning over complex pipeline behavior, failure patterns, or governance constraints. This paper introduces a systemic, AI-driven DevOps automation framework designed to optimize CI/CD pipelines through continuous learning, risk-aware decision-making, and policy-aligned control. The core contribution is a closed-loop, intelligence-driven control plane that integrates telemetry inference, pipeline behavior modeling, and constrained decision automation to optimize build reliability, deployment throughput, and operational toil while preserving human oversight and enterprise governance. Unlike existing approaches that focus on isolated optimizations or tool-level enhancements, the proposed framework treats CI/CD as a distributed socio-technical system, addressing failure modes related to scale, drift, cognitive load, and compliance. We describe the architecture, lifecycle control flow, and governance mechanisms of the proposed system, and evaluate its impact using operational metrics such as mean time to detection (MTTD), mean time to recovery (MTTR), pipeline failure recurrence, and policy deviation rates. The results demonstrate that AI-driven DevOps automation, when designed as a governed control system rather than an autonomous executor, can materially improve reliability, safety, and delivery efficiency in enterprise CI/CD environments.
Co-Authors Kakarla, Roshan Sunkari, Suresh