Efficient AI-Driven Data Compression Techniques for Large-Scale Distributed Systems

Abstract

With the exponential growth of data in large-scale distributed systems, efficient data compression techniques are essential for optimizing storage, transmission, and computational efficiency. Traditional compression methods often struggle to balance compression ratio, processing speed, and data fidelity, particularly in real-time and high-throughput environments. This paper proposes an AI-driven data compression framework that leverages deep learning-based models, including autoencoders, recurrent neural networks (RNNs), and transformer-based architectures, to achieve adaptive and lossless compression in distributed systems. The proposed approach dynamically adjusts compression levels based on data characteristics, workload demand, and network conditions, ensuring optimal performance across cloud, edge, and IoT infrastructures. Experimental evaluations on large-scale datasets demonstrate that the AI-driven method achieves a 60% higher compression ratio and a 40% reduction in computational overhead compared to conventional techniques such as Huffman coding and Lempel-Ziv-Welch (LZW) compression. Moreover, the system maintains low latency and high data retrieval accuracy, making it suitable for real-time applications in cloud storage, big data analytics, and distributed computing environments. This study provides a novel contribution to AI-augmented compression techniques, enabling scalable and efficient data management for next-generation distributed systems.