Data Integrity and Access Optimization in NAS Architectures using Advanced Signal Processing Techniques
Keywords:
Data Integrity, Access Optimization, NAS Architectures, Signal Processing, Error Correction, Machine Learning, Latency Reduction, System Efficiency, Anomaly Detection, Sustainable Computing.Abstract
The rapid growth of digital data has intensified the need for robust Network-Attached Storage (NAS) architectures that ensure data integrity while optimizing access efficiency. This study proposes a novel framework that integrates advanced signal processing techniques—including error correction coding, digital filtering, and machine learning-based anomaly detection—to address these challenges. The framework is designed to enhance data integrity by improving error detection and correction rates, reduce data retrieval latency, and optimize system efficiency. Experimental results demonstrate significant improvements, with error detection and correction rates increasing to 98.7% and 96.3%, respectively, and average latency decreasing by 32.5%. Throughput improved by 29.5%, while system efficiency rose to 91.2%. Machine learning algorithms achieved an anomaly detection accuracy of 94.8%, enabling proactive maintenance and reducing system downtime by 63.2%. Additionally, the framework reduced storage overhead by 22.7% and energy consumption by 12.2%, highlighting its cost-effectiveness and sustainability. Statistical analyses, including paired t-tests and regression models, confirm the significance of these improvements. The proposed framework offers a scalable and efficient solution for modern NAS architectures, addressing critical challenges in data storage and retrieval. Its integration of advanced signal processing techniques provides a balanced approach to maintaining data integrity and optimizing access, making it a valuable tool for industries reliant on high-performance storage systems.
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