Cognitive Cloud Management: Leveraging Multi-Modal Learning for Intelligent Resource Optimization and Fault Resolution
Abstract
Cloud infrastructure management requires intelligent and adaptive strategies to handle the increasing complexity and dynamic nature of modern cloud environments. This paper proposes a Cognitive Cloud Management (CCM) framework that leverages multi-modal learning to enhance resource optimization and fault resolution. The proposed system integrates natural language understanding (NLU) with real-time monitoring and structured data analysis to create a comprehensive decision-making model. By combining human-like understanding of user queries with data-driven insights from system metrics, the framework can detect anomalies, predict failures, and optimize resource allocation in real-time. The CCM architecture uses deep learning models for NLU and streaming data processing to provide rapid, context-aware responses to infrastructure changes. Experimental results demonstrate improved system performance, including reduced latency, higher fault detection accuracy, and better overall resource utilization. The proposed approach represents a significant step toward autonomous and intelligent cloud infrastructure management by fusing cognitive and analytical capabilities.
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