GenAI-Driven Observability and Incident Response Control Plane for Cloud-Native Systems
DOI:
https://doi.org/10.15662/IJRAI.2024.0706027Keywords:
GenAI, observability, incident response, AIOps, SRE, telemetry correlation, root cause analysis, cloud reliability, LLMs, autonomous operationsAbstract
Modern cloud-native systems generate massive volumes of telemetry in the form of metrics, events, logs, and traces (MELT). While observability platforms have significantly improved visibility into distributed systems, incident response in large-scale environments remains heavily manual, reactive, and dependent on human interpretation. Site Reliability Engineering (SRE) teams are frequently overwhelmed by alert fatigue, fragmented signals, and delayed root cause identification, resulting in prolonged mean time to detection (MTTD) and mean time to resolution (MTTR).
This paper presents a GenAI-Driven Observability and Incident Response Control Plane designed to transform observability from a passive monitoring capability into an active, intelligent decision-making system. The proposed framework integrates large language models (LLMs), machine reasoning, and telemetry correlation engines to continuously interpret system behavior, synthesize contextual insights, and assist or automate incident response workflows. Unlike traditional AIOps systems that rely on static rules or narrow statistical models, this approach leverages GenAI to reason across heterogeneous telemetry, historical incidents, architectural knowledge, and operational runbooks.
The control plane introduces a layered architecture that combines real-time telemetry ingestion, semantic signal enrichment, GenAI-based incident interpretation, and policy-driven response orchestration. By embedding reasoning capabilities directly into the observability pipeline, the framework enables proactive anomaly detection, contextual root cause analysis, and guided remediation across complex cloud and microservices environments. This work demonstrates how GenAI can significantly reduce operational toil, improve response consistency, and enhance system resilience while preserving human oversight and regulatory controls in production systems
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