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<p>As social and economic development advances, the large and diverse volume of data presents significant challenges to computer systems, particularly in data processing. Traditional data processing model often lack scalability, as they are designed to handle smaller, structured datasets, resulting in delays and higher processing costs when managing the vast data produced in today’s digital environment. This extra overhead is caused by increased computational demands, storage management, and data transfer, which impair system performance. To overcome this problem, this research introduces smart storage-side data processing, with the goal of reducing processing overhead and enhancing scalability. The simulation results prove that the proposed smart storage-side data processing may optimize the data processing performance of the computer system, reduce computational costs in terms of time, and improve scalability compared to traditional models.</p>

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Analysis of the construction of a smart storage-side data proposal model in computer systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • Pengteng Huang,
  • Xiaodong Xie,
  • Liwei Tian,
  • Yantao He,
  • Janaka Alawatugoda

摘要

As social and economic development advances, the large and diverse volume of data presents significant challenges to computer systems, particularly in data processing. Traditional data processing model often lack scalability, as they are designed to handle smaller, structured datasets, resulting in delays and higher processing costs when managing the vast data produced in today’s digital environment. This extra overhead is caused by increased computational demands, storage management, and data transfer, which impair system performance. To overcome this problem, this research introduces smart storage-side data processing, with the goal of reducing processing overhead and enhancing scalability. The simulation results prove that the proposed smart storage-side data processing may optimize the data processing performance of the computer system, reduce computational costs in terms of time, and improve scalability compared to traditional models.