## 2 Related Works
### Multi-Tiered Storage Systems  
[PERSON] et al. [21] proposes prefetching data blocks by analyzing the block access pattern per application context in a storage area network. It analyzes the frequency of access order of data blocks and detects correlation of access history. AMP [22] prefetches metadata based on affinity of metadata access patterns in distributed storage, where metadata servers and file servers are separated. In the active archive system [23; 24], it is possible to repeatedly schedule the archiving start point to be performed at a specific time. The archive target file can be set by the elapsed time since the last access or can be based on file attribute values such as file size, file owner, and file type. Because the active archive system restores files to main storage when accessing a link (or an online mode file), there is a recovery delay for all archived file accesses. Amazon S3 Intelligent-Tiering [25] organizes storage with three or four layers and moves old files from higher layer to lower layer storage. When a file in the lower layer storage is accessed, it moves to the top layer (Frequent Access Tier) storage. As with the previous active archive systems, Amazon S3 also has the same problem of restoring files when they are accessed. [PERSON] et al. [26] proposed a file prefetching method for multi-tiered archive storage systems. They select target files by applying machine learning to the metadata of accessed files to predict future access patterns. File prefetching also has been researched in the multi-tiered storage of high performance computing (HPC) systems [27; 28]. [PERSON] et al. [27] proposed multilayered buffer storage (MLBS), which is a data management method in three layered storages of super computers for HPC. MLBS prefetches data if higher layered storage has an empty buffer slot. Selection of prefetched files is based on whether the application processes in LIFO or FIFO. In the research of [PERSON] et al. [28], a HPC client of hierarchical storage management (HSM) architecture uses its SSD as cache. Files to be cached are selected using file access information such as open/close events, client NID, and job ID. Some research approaches prefetching data in a more fine-grained manner [29; 30]. In the research of [PERSON] et al. [29], they consider the typical access patterns of different file types when file pages are prefetched to the cache in the OS level. Prefetch window size is adjusted according to the file type. [PERSON] et al. [30] proposed a method to prefetch file segments in multi-tier storages, which is called HFetch. HFetch specifically focuses on a write-once-read-many (WORM) data access model of scientific data workflow. After HFetch detects file access, it prefetches the next file segment by analyzing the global view of the file access pattern with updated file offset, length, and timestamp. Multi-tiered storage systems that support file cache or prefetch are similar to the transition to the local mode of the proposed mechanism, although their target domains are different. While caching takes place in an on-demand manner, prefetching occurs in advance before the file is accessed. Table 1 summarizes their comparison in various aspects. In the existing multi-tiered storage systems, cache hit ratio and prefetching accuracy are the main important performance factors because the cache and prefetch target is costly high-layer storage (main purpose and storage hierarchy columns in Table 1). In the proposed file synchronization system, on the other hand, as client and server storages are not hierarchical and they are main storages used by a user, the selection accuracy of local mode candidate files is not relatively important. Therefore, the proposed file mode change mechanism uses a directory activation ratio as the criterion for changing a file to the local mode, which does not require as much computing resources and historical information as prefetching methods for multi-tiered storage systems.