AI Enabled Predictive Monitoring and Self Healing DevOps Architectures for High Availability Cloud Computing Platforms

Abstract

High-availability cloud platforms increasingly depend on predictive telemetry engines that attempt to identify operational degradation before cascading service collapse emerges across distributed containers, orchestration layers, and software-defined infrastructure. Existing DevOps pipelines, despite years of automation rhetoric, remain structurally reactive; they detect instability after latency amplification, memory leakage, thread starvation, or API congestion has already entered user-facing execution paths. The friction lies in the false assumption that observability stacks automatically produce resilience. They do not. AI-enabled predictive monitoring architectures instead reposition anomaly detection as a probabilistic governance layer operating above infrastructure telemetry, integrating recurrent neural forecasting, reinforcement-based remediation, and adaptive rollback orchestration to reduce mean time to recovery within volatile cloud ecosystems. The evidence is contradictory at best. Several enterprise deployments reported measurable reductions in outage duration, yet parallel studies exposed unacceptable false-positive escalation rates, remediation oscillation, and hidden computational overhead generated by autonomous healing agents themselves.

Self-healing DevOps architectures evolved through fragmented experimentation rather than coherent systems engineering doctrine, producing infrastructures overloaded with redundant alerting pipelines, unstable remediation triggers, and decision-making opacity embedded within machine-learning classifiers. Short-term gains appeared attractive. Long-term operational entropy persisted. Contrary to established norms, the strongest architectures were not those with maximal automation density, but those implementing constrained autonomy, failure containment zoning, and selective rollback governance. This forces a choice between operational speed and infrastructural interpretability. The reality is simpler: autonomous remediation without contextual reasoning frequently magnifies outage propagation.