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%.

While low-density parity-check code has been adopted in 3-D flash for improving chip reliability, it suffers from severe read latency due to the increasing number of read retries. Recent studies propose read-level recording to mitigate the performance loss from failed read retries. However, existing schemes induce large updating overhead and achieve suboptimal results, making it critical to develop better tradeoffs among storage overhead, process variation, and performance improvement. In this article, we propose AR$^{2}$, an adaptive read-level recording scheme to improve read performance for 3-D NOT AND (NAND) flash. It consists of two designs: AR$^{2}$-win and AR$^{2}$-pre. AR$^{2}$-win records the number of read levels that fit the majority of the last$N$reads, which prevents the worst page from dominating the read level recording. AR$^{2}$-pre predicts the number of read levels for the next read based on the recorded one and a simple machine learning model, which prevents using stale recorded levels in large-capacity solid-state drives (SSDs). Our experimental results show that AR$^{2}$significantly improves the read performance for 3-D NAND flash and achieves on average 15% or more read latency reduction over the state-of-the-art.

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.