Intelligent Cloud Native DevOps Frameworks for Autonomous Infrastructure Management and AI Driven Service Orchestration in Hybrid AWS and Azure Ecosystems
Keywords:
Cloud Native DevOps, AIOps, Hybrid Cloud Orchestration, Autonomous Infrastructure Management, Kubernetes, GitOps, AWS, Azure, Service Mesh, Predictive RemediationAbstract
Cloud native DevOps systems operating across AWS and Azure environments have been marketed as self-healing computational fabrics capable of autonomous orchestration, elastic remediation, and predictive infrastructure governance. The evidence is contradictory at best. Most enterprise deployments still depend on fragmented observability stacks, manually tuned Kubernetes policies, and brittle CI/CD chains that collapse under state inconsistency, service drift, or cross-cloud latency amplification. Autonomous infrastructure management remains trapped between algorithmic optimism and operational entropy, since AI-driven orchestration engines frequently inherit the same institutional failures embedded within legacy governance structures. Short-term elasticity appears impressive during benchmark simulations. Production systems behave differently. Hybrid cloud ecosystems expose hidden synchronization delays, opaque cost propagation, identity sprawl, and feedback-loop instability that conventional DevOps literature continues to understate.
The proposed framework integrates AIOps telemetry, GitOps reconciliation models, Kubernetes-native orchestration, and reinforcement-based remediation logic into a distributed hybrid control plane spanning AWS and Azure infrastructures. The friction lies in coordination failure rather than computational scarcity, since orchestration intelligence degrades rapidly once heterogeneous APIs, conflicting security abstractions, and asynchronous infrastructure states begin interacting under load volatility. This paper evaluates those tensions through a comparative architectural methodology grounded in scholarship, simulated orchestration metrics, and systemic failure analysis.
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