Multi-Modal Context Fusion for Cloud Infrastructure Management: Integrating Natural Language Understanding with Real-Time Resource Analytics
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Abstract
Effective management of cloud infrastructure requires a comprehensive understanding of both system metrics and user intent. This paper presents a novel Multi-Modal Context Fusion (MMCF) framework that integrates Natural Language Understanding (NLU) with real-time resource analytics to enhance cloud infrastructure management. The proposed architecture combines insights from user queries and system logs, enabling more accurate and context-aware decision-making. By fusing structured data from real-time monitoring tools with unstructured language-based input, the MMCF framework improves anomaly detection, resource optimization, and fault resolution. The system leverages deep learning models for NLU and real-time streaming data processing to provide adaptive responses to dynamic cloud environments. Experimental results demonstrate significant improvements in system performance, including reduced downtime, enhanced scalability, and more efficient resource utilization. The proposed approach represents a significant step toward intelligent, autonomous cloud infrastructure management by integrating human-like understanding with data-driven insights.
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References
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