Cognitive CloudOps: Integrating Generative AI for Predictive Infrastructure Management and Self-Optimizing DevOps Pipelines

The relentless escalation of architectural complexity in cloud-native environments has necessitated a paradigmatic shift from traditional imperative automation to cognitive, self-optimizing operations. This research presents a comprehensive Cognitive CloudOps framework, articulating the integration of Large Language Models (LLMs) and Reinforcement Learning (RL) into the core of the software development life cycle. By synthesizing multi-modal observability data—encompassing logs, metrics, traces, and hardware telemetry—the framework enables autonomous root cause analysis (RCA), predictive fault detection, and real-time infrastructure remediation. Central to this study is the evaluation of LogSage, an end-to-end LLM-powered diagnostic system that achieves 98% precision in identifying CI/CD failures through token-efficient preprocessing and multi-route Retrieval-Augmented Generation (RAG). Furthermore, the research delineates a five-plane architectural blueprint for enterprise-grade cognitive control planes, ensuring that AI-driven orchestration remains grounded in ethical governance and "Compliance-as-Code" via the Virelya framework. Empirical evidence from large-scale industrial deployments indicates that cognitive adaptation can reduce Mean Time to Resolution (MTTR) by up to 65% and noise in monitoring signals by 99%. This study provides the foundational theoretical and practical evidence required for a six-page submission to premier venues such as IEEE Transactions on Mobile Computing or ACM Transactions on Interactive Technologies, marking a definitive step toward the realization of a truly autonomous, self-learning cloud ecosystem.