High-capacity solid-state drives (SSDs) with expected capacities of one PByte and more will address cloud storage and archiving environments previously dominated by magnetic disks. These new applications are very cost-sensitive, so unnecessary overhead must be reduced as much as possible without sacrificing the performance advantages of SSDs. An expensive component within a scale-up SSD is the on-device memory to map host logical page numbers to flash pages. This article, therefore, proposes HyTorC, which builds on the idea of S-FTL to represent sequentially stored logical pages using bitmaps instead of providing one entry per logical page. HyTorC extends this idea by introducing, for the first time in an FTL, compression of these bitmaps using run-length encoding, and by investigating the effects of background scrubbing to realign randomly written pages into contiguous runs. This background scrubbing allows HyTorC to keep large portions of the mapping table in block mapping mode, further reducing the memory footprint. HyTorC retains the flexibility of the page mapping scheme and supports compression of logical blocks within the SSD, allowing multiple compressed logical pages to be stored within a single physical page. HyTorC is fully implemented in an open-channel SSD. Our tests show that HyTorC can reduce memory consumption by an average of 98.9% over standard page mapping, 95.6% over the DFTL scheme, 87.3% over S-FTL, and 56.5% over the learned index-based approach LeaFTL for the Alibaba Cloud block traces, and by 98.6% over standard page mapping, 95.5% over the DFTL scheme, 84.1% over S-FTL, and 61.5% over LeaFTL for the Microsoft Research Cambridge traces. HyTorC focuses on the memory footprint of the FTL and not on performance. However, the performance evaluation shows that HyTorC achieves similar performance compared to page mapping and FTLs based on learned indexes.

Traditional DRAM-based memory solutions face challenges, including high energy consumption and limited scalability. Non-Volatile Memory (NVM) offers low energy consumption and high scalability. However, security challenges, particularly data remanence vulnerabilities, persist. Prevalent methods such as the Bonsai Merkle Tree (BMT) are employed to ensure data security. However, the consistency requirements for integrity tree updates have led to performance issues. It is observed that compared to a secure NVM system without persistent secure metadata, the average overhead for updating and persisting the BMT root with persistent secure metadata is as high as 2.48 times. Therefore, this paper aims to mitigate these inefficiencies by leveraging the principle of memory access locality. We propose the Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree (DCAS-BMT). The DCAS-BMT is dynamically built and continuously adjusted at runtime according to access weights, ensuring frequently accessed memory blocks reside on shorter paths to the root node. This reduces the verification steps for frequently accessed memory blocks, thereby lowering the overall cost of memory authentication and updates. Experimental results using the USIMM memory simulator demonstrate that compared to the widely used BMT approach, the DCAS-BMT scheme shows a performance improvement of 34.1%.