Abstract: When handling large-scale data in distributed file system (DFS), metadata management poses a critical challenge. Metadata operations account for the majority of file system operations, so enhancing the performance of metadata services is of utmost importance. Traditional metadata access methods suffer from issues such as network latency and server load, resulting in inefficiency. To address these problems, research has been conducted on DFS-based metadata prefetching strategies, including prefetching based on access patterns, caching mechanisms, and prediction models. These strategies reduce latency and improve I/O efficiency by proactively caching metadata that is about to be used. However, prefetching strategies face challenges related to prediction accuracy, cache management, data consistency, and security. Future development directions include prefetching strategies based on deep learning and intelligent algorithms, as well as the adaptive and dynamically adjusted prefetching strategies. These strategies will contribute to enhancing the efficiency and accuracy of metadata management, thereby meeting the ever-increasing storage demands in the era of big data, with metadata prefetching strategies playing a crucial role in this process.

Key words: distributed file system (DFS), metadata, metadata management, prefetching strategy