From Reactive Alerts to Predictive Intelligence: AI-Assisted Monitoring in Modern Cloud Environments
DOI:
https://doi.org/10.15662/IJRAI.2023.0601009Keywords:
AI-assisted monitoring, proactive incident detection, anomaly detection, AIOps, log analytics, Isolation Forest, Drain, statistical learning, predictive monitoring, observabilityAbstract
Modern distributed systems operating across cloud-native, microservices-based, and containerized environments generate massive volumes of high-velocity telemetry data, including logs, metrics, traces, configuration changes, and event streams. As system architectures grow increasingly decentralized and elastic, traditional rule-based monitoring approaches dependent on static thresholds and manually crafted alerts struggle to cope with dynamic workloads, ephemeral infrastructure, and evolving failure modes. These limitations often result in excessive false positives, alert fatigue, delayed root cause identification, and reactive incident management practices. Artificial Intelligence (AI) and Machine Learning (ML) techniques have therefore emerged as critical enablers of proactive incident detection, leveraging unsupervised, semi-supervised, and statistical learning models to automatically identify anomalous behavior patterns before they escalate into service degradation or outages. By incorporating scalable anomaly detection algorithms such as Isolation Forest, structured log parsing mechanisms like Drain, and real-time statistical learning frameworks for continuous anomaly scoring, AI-assisted monitoring systems transform raw telemetry into actionable insights. This paper presents a comprehensive overview of these AI-driven monitoring architectures, examining their algorithmic foundations, data preprocessing pipelines, streaming inference capabilities, and operational trade-offs, while highlighting how they collectively support early detection, adaptive learning, and resilience in modern production environments.
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