在分布式文件系统中处理大规模数据时，元数据管理是关键挑战。元数据操作占文件系统操作的大部分，因此提升元数据服务性能至关重要。传统元数据访问方式存在网络延迟和服务器负载问题，导致效率低下。为了解决这些问题，研究基于DFS的元数据预取策略，包括基于访问模式、缓存机制和预测模型的预取。这些策略通过提前缓存即将使用的元数据，降低延迟，提高I/O效率。然而，预取策略面临预测准确性、缓存管理、数据一致性和安全性挑战。未来的发展方向包括基于深度学习和智能化算法的预取策略，以及自适应和动态调整的预取策略。这些策略将有助于提高元数据管理的效率和准确性，从而在大数据时代满足日益增长的存储需求，使得元数据预取策略在其中发挥至关重要的作用。

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.