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 write fds. On the one hand, fds and files are mapped many-to-many, so there is an issue in mapping reserved files according to reserved fds that a normal file is selected as a reserved file. On the other hand, the mapping of files to fds for big data traces is confusing. We propose ARST that traces the file of write fd to 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.