Designing Scalable AI Systems for Continuous Monitoring, Fault Detection, and Operational Intelligence
DOI:
https://doi.org/10.64137/31079911/IJMST-V2I1P105Keywords:
AIOps, observability, fault detection, operational intelligence, microservices, anomaly detection, decision intelligence, software reliability, trace analytics, cloud-native AI systemsAbstract
Modern digital platforms are expected to operate continuously across heterogeneous clouds, microservices, data pipelines, AI-enabled applications, and cyber-physical workflows. In such environments, conventional monitoring is no longer sufficient because it reports isolated symptoms instead of generating system-level understanding. This paper proposes an architecture-centered research framework for scalable AI systems that unify continuous monitoring, fault detection, and operational intelligence into a closed-loop capability. The framework integrates telemetry collection, semantic data normalization, learning-based anomaly detection, graph-based dependency reasoning, and policy-aware decision intelligence so that systems can move from reactive alerting to evidence-based adaptation. The paper synthesizes advances in observability engineering, log intelligence, software defect prediction, automated testing, vulnerability detection, and domain-specific operational analytics to define a layered reference architecture suitable for software-intensive and distributed environments. Rather than presenting fabricated benchmark outcomes, the paper contributes a research-grade design blueprint, a model taxonomy, and an evaluation agenda for future empirical validation. The framework is intentionally cross-domain: it can support cloud-native software systems, healthcare and pharmacy operations, customer AI platforms, financial systems, manufacturing, and supply-chain networks. The paper argues that scalable operational intelligence emerges when telemetry, prediction, and action are treated as a single architectural problem rather than as disconnected toolchains.
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