As we all know, file and free space fragmentation negatively affect file system performance. F2FS is a file system designed for flash memory. However, it suffers from severe fragmentation due to out-of-place updates and the highly synchronous, multithreaded writing behaviors of applications. The adaptive reserved space (ARS) scheme chooses some files instead of all files to update in the reserved space which collects file features associated with fragmentation to construct datasets and uses decision trees to pick reserved files. However, ARS processes file features according to writefds. On the one hand,fdsand files are mapped many-to-many, so there is an issue in mapping reserved files according to reservedfdsthat a normal file is selected as a reserved file. On the other hand, the mapping of files tofdsfor big data traces is confusing. We propose ARST that traces the file of writefdto optimize ARS. Moreover, by selecting time-independent file features, ARST predicts whether files with little historical information are reserved to maximize the performance improvement brought by reserving space. Besides, adjustable reserved space and dynamic reservation strategies are adopted. We implement ARST on a HiKey960 development platform and a commercial smartphone with slight space and file creation time overheads. Experimental results show that ARST reduces file and free space fragmentation dramatically and improves file system performance. ARST reduces the running time of realistic workloads by up to 94.28% than F2FS with in-place updates and outperforms ARS by up to 49.06% for Wechat.

With the widespread use of flash memory from mobile devices to large data centers, flash friendly file system (F2FS) designed for flash memory features has become popular. However, F2FS suffers from severe cleaning overhead due to its logging scheme writes. Mixed storage of data with different hotness in the file system aggravates segment cleaning. We propose multi-log delayed writing and modified segment cleaning based on dynamically identified hotness (M2H). M2H defines the hotness by the file block update distance and uses the K-means clustering to identify hotness accurately for dynamic access patterns. Based on fine-grained hotness, we design multi-log delayed writing and modify the selection and release of the victim segment. The hotness metadata cache is used to reduce overheads induced by hotness metadata management and clustering calculations. Compared with the existing strategy of F2FS, migrated blocks of segment cleaning in M2H reduce by 36.05% to 36.51% and the file system bandwidth increases by 69.52% to 70.43% cumulatively.

As we all know, file and free space fragmentation negatively affect file system performance. F2FS is a file system designed for flash memory. However, it suffers from severe fragmentation due to its out-of-place updates and the highly synchronous, multi-threaded writing behaviors of mobile applications. We observe that the running time of fragmented files is 2.36× longer than that of continuous files and that F2FS's in-place update scheme is incapable of reducing fragmentation. A fragmented file system leads to a poor user experience.(p)(/p)Reserving space to prevent fragmentation is an intuitive approach. However, reserving space for all files wastes space since there are a large number of files. To deal with this dilemma, we propose an adaptive reserved space (ARS) scheme to choose some specific files to update in the reserved space. How to effectively select reserved files is critical to performance. We collect file characteristics associated with fragmentation to construct data sets and use decision trees to accurately pick reserved files. Besides, adjustable reserved space and dynamic reservation strategy are adopted. We implement ARS on a HiKey960 development platform and a commercial smartphone with slight space and file creation time overheads. Experimental results show that ARS reduces file and free space fragmentation dramatically, improves file I/O performance and reduces garbage collection overhead compared to traditional F2FS and F2FS with in-place updates. Furthermore, ARS delivers up to 1.26× transactions per second under SQLite than traditional F2FS and reduces running time of realistic workloads by up to 41.72% than F2FS with in-place updates.