Recently, the emerging Interlaced Magnetic Recording (IMR) technology has substantially improved the areal density of disks by implementing interlaced track layout. While this track layout enhances disk storage capacity, it impairs the flexibility of write positioning. To maintain stable write performance of IMR disks, operations such as Read-Modify-Write (RMW) or Garbage Collection (GC) must be introduced, which inevitably incur extra I/Os. Especially in write-intensive workloads, the excessive additional I/Os exacerbate the write amplification effect, thereby severely degrading the overall performance of IMR disks. Although existing data management strategies strive to reduce extra I/Os via device drivers or system middleware, the semantic disparity between the disk and file system inherently limits these strategies to achieve optimal performance. To address the aforementioned challenge,this paper proposes IMR-Friendly File System (IFFS), an innovative file system tailored for IMR disks. First, we propose a semantic-aware hotness identification algorithm based on Online K-means, which redefines the data hotness metric by exploiting file system semantics to reduce data migration induced by inaccurate data classification. Second, we introduce an I/O-overhead-minimized data placement strategy that adaptively selects between in-place and out-of-place writing modes based on data hotness metrics. Furthermore, this strategy employs a log-transition mechanism to dynamically adjust write positions, effectively mitigating I/O overhead caused by RMWs and scattered read requests. Finally, we implement a multi-factor garbage collection mechanism that incorporates intra-zone data hotness, data layout, fragmentation levels, and other contextual attributes to optimize file system data management efficiency, thereby enhances the read performance on IMR disk-based storage system performance. Experimental results show that IFFS achieves an average bandwidth improvement of 12.96× over EXT4, XFS, F2FS, and the state-of-the-art work in Fio evaluation. Under YCSB workloads, IFFS improves bandwidth by an average of 56.89% while reducing latency by 34.46%.

The emerging Interlaced Magnetic Recording (IMR) technology not only achieves higher storage density than SMR, but also significantly reduces rewrite overhead by dividing tracks into bottom and top tracks and organizing them in an interlaced fashion. However, frequent updates to the bottom track can trigger a large number of Read-Modify-Write (RMW) operations during high disk space utilization, which can severely degrade the I/O performance. Addressing this issue, this paper proposes an interlaced translation layer named DTB+ to improve the write performance of IMR disks. Firstly, a workload-sensitive track heat analysis mechanism is introduced to intelligently place data to reduce track rewrite probability. Simultaneously, the zero-incremental cost region is selectively used to construct a twin-buffer architecture to reduce RMW operations. In addition, an adaptive space allocation engine based on reinforcement learning was developed to flexibly allocate and reclaim space within the twin-buffer, improving disk resource utilization . Finally, establish a flexible evicted-data transfer zone to delay the writeback operations of interference data, further reducing the additional overhead. Experimental results indicate that compared with the state-of-the-art studies, DTB+ can reduce RMWs by 63.00% and additional I/O operations by 57.41%, decrease the average write latency by 37.77%, and lower the tail latency by 53.95%.

Shingled Magnetic Recording (SMR) technology, employing a shingled track layout, has significantly enhanced areal density capability. However, this layout imposes severe write penalties when dealing with non-sequential writes. The emerging Interlaced Magnetic Recording (IMR) technology not only achieves higher storage density than SMR, but also significantly reduces rewrite overhead by dividing all tracks into bottom and top tracks and organizing them in an interlaced fashion. However, frequent updates to the bottom track can trigger a large number of Read-Modify-Write (RMW) operations during high disk space utilization, which can severely affect the I/O performance of the disk. Addressing this issue, this paper proposes an interlaced translation layer named DTB to improve the write performance of IMR disks. Firstly, a workload-sensitive track heat analysis mechanism is introduced to intelligently place data to reduce track rewrite probability. Simultaneously, the zero-incremental cost region is selectively used to construct a Twin-Buffer architecture to reduce RMW operations triggered by frequent writeback of hot data, thereby effectively curtailing the rewriting overhead. In addition, an adaptive space allocation engine was developed by analyzing the data characteristics in the buffer, and we designed a dynamic configuration model based on reinforcement learning to flexibly allocate and reclaim space within the Twin-Buffer, improving disk resource utilization and I/O performance. Experimental results indicate that DTB can reduce the number of RMWs by 63.45%, decrease the average write latency by 44.37%, and lower the tail latency by 56.84% compared with state-of-the-art studies.

3D charge-trap (CT) NAND flash-based SSD has been used widely for its large capacity, low cost per bit, and high endurance. One-shot program (OSP) scheme, as a variation of incremental step pulse programming (ISPP) scheme, has been employed to program data for CT flash, whose program unit is the Word-Line (WL) instead of the page. The existing program optimization schemes either make trade-offs among program latency and reliability by adjusting the program step voltage on demand; or remap the most error-prone cell states to others by re-encoding programmed data. However, the data pattern, which represents the ratio of 1s in data values, has not been thoroughly studied. In this paper, we observe that most small files do not contain uniform 1s and 0s among these common file types (i.e., image, audio, text, executable file), leading to programming WL cells in different states unevenly. Some cell states dominate over the WL, while others are not. Based on this observation, we propose a flexible reliability enhancement scheme based on the OSP scheme. This scheme programs the cells into different states with varied , i.e., these cells in one state, whose number is the largest in one WL, are programmed with a fine-grained (namely slow write). In contrast, the minority are programmed with a coarse-grained (namely fast write). So the reliability is improved due to averaging the major enhanced cells with the minor degraded cells without program latency overhead. A series of experiments have been conducted, and the results indicate that the proposed scheme achieves 34% read performance improvement and 16% lifetime elongation on average.