基于 Apriori 的预取文件缓存

Prajwal Said, Ketaki Naik, Nupur Agrawal, Srushti Bhoite, Sayali Shelar
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引用次数: 0

摘要

该项目提出了一个创新解决方案,旨在通过与图形用户界面(GUI)集成的预测缓存技术优化文件系统性能。图形用户界面通过提供浏览文件和通过图形表示带宽和每秒输入/输出操作(IOPS)显示性能指标等功能来促进用户互动。该功能围绕动态确定文件在固态硬盘(SSD)或硬盘驱动器(HDD)上的位置。该系统采用预测缓存来识别频繁访问的文件,通过将其存储在固态硬盘上确保更快的检索速度。相反,访问频率较低的文件则分配到硬盘上。该项目利用 Flexible I/O (fio) 工具来测量在固态硬盘和硬盘上访问文件的性能。此外,为了深入了解数据集中不同文件之间的关系,该项目采用了 Apriori 算法。通过分析结构关系,该算法为优化文件放置决策、提高整体系统效率和调整缓存策略以适应不断变化的访问模式提供了宝贵的情报。通过根据访问模式动态调整文件放置策略,并利用先进的算法进行智能决策,该系统在管理文件操作方面努力提升用户体验和系统效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Apriori-Based Prefetching Files for Caching
The project proposes an innovative solution aimed at optimizing file system performance through predictive caching techniques integrated with a Graphical User Interface (GUI). The GUI facilitates user interaction by offering functionalities such as browsing files and displaying performance metrics via graphical representations of bandwidth and Input/Output Operations Per Second (IOPS). The functionality revolves around dynamically determining file placement on Solid State Drives (SSDs) or Hard Disk Drives (HDDs). The system employs predictive caching to identify frequently accessed files, ensuring faster retrieval by storing them on SSDs. Conversely, less frequently accessed files are allocated to HDDs. The project utilizes the Flexible I/O (fio) tool to measure the performance of files accessed on both SSDs and HDDs. Furthermore, to obtain insights and relationships between different files within the dataset, the project incorporates the Apriori algorithm. By analyzing structured relationships, the algorithm provides valuable intelligence for optimizing file placement decisions, enhancing overall system efficiency and adjust caching strategies to adapt to changing access patterns. By dynamically adapting file placement strategies based on access patterns and leveraging advanced algorithms for intelligent decision-making, the system endeavors to enhance user experience and system efficiency in managing file operations.
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