Persistent memory (PM) offers a compelling combination of durability and near-DRAM performance, but it also introduces new challenges for hashing indexes. Existing persistent hashing designs prioritize resizing efficiency at the expense of increased query latency, losing the key advantage of hash tables. This paper introduces$\mathbf{R}^{2}$Hash, a persistent hashing index redesigned from the persistent cache-line hash table, to balance high read performance with efficient resizing.$R^{2}$Hash is guided by a migration rule, enabling it to meet both goals through two main contributions: (i) a cooperative and lowoverhead resizing strategy based on split-order hashing, and (ii) a shift-aware search combined with a two-layer bucket layout that enables lock-free reads with only one PM access on average. Furthermore,$\mathbf{R}^{2}$Hash provides log-free consistency and a nonblocking recovery mechanism. Experimental results demonstrate that$\mathbf{R}^{\mathbf{2}}$Hash achieves up to$8.1 \times$higher search throughput and$7.5 \times$higher insert throughput compared to other persistent hash indexes across a range of workloads.

The Zoned Namespace (ZNS) interface transfers most storage maintenance responsibilities from the underlying Solid-State Drives (SSDs) to the host. This shift creates new opportunities to ensure fairness and high performance in multi-tenant cloud computing environments at both hardware and software levels. However, when applications with different workloads share a single ZNS SSD hardware, traditional fair queueing schedulers fail to achieve fairness due to their limited awareness of workload characteristics. Moreover, allowing multiple outstanding requests to access the device simultaneously improves resource utilization but often leads to significant I/O interference among these requests. This interference results in over-throttling, which subsequently degrades the performance of existing fair queueing schedulers. To address the above problems, this article proposes an efficient and high-performance fair queueing scheduling scheme for ZNS SSD (ZNSFQ) on the host side. Firstly, ZNSFQ introduces a workload-aware fair scheduler that enhances fairness by accurately estimating the I/O cost for each application based on its workload characteristics. Secondly, to optimize performance while ensuring fairness, ZNSFQ designs a request dispatch parallelism adjuster. This adjuster manages the channel-level request dispatch parallelism for each application to minimize I/O interference. Finally, ZNSFQ employs a global adaptive coordinator to alleviate device-level I/O blocking, reducing tail latency and CPU consumption while satisfying fairness and performance. A comprehensive evaluation demonstrates that ZNSFQ significantly enhances fairness and performance compared to the latest fair queuing schedulers. In sequential access scenarios, ZNSFQ enhances fairness by over 38.13% and increases I/O bandwidth by more than 49.24%. Furthermore, in random access scenarios, it reduces CPU utilization by 70.22% while maintaining both fairness and high performance.

Data migration strategies (DMS) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS -hot data selection, migration granularity, and migration frequency -are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in sub-optimal performance and unnecessary migration traffic. In this paper, we propose AdaptHM, a fully access-pattern-aware Adaptive data migration strategy for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multi-level data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme.

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating$\mathrm{B}^{+}$-Tree indexes. However, existing persistent$\mathrm{B}^{+}$-Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance$\mathrm{B}^{+}$– Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to$4.38\times$higher write throughput and up to$62\times$lower tail latency compared to state-of-the-art persistent$\mathrm{B}^{+}$-Tree indexes.

Tree index structures are widely employed in modern storage systems to support high-performance queries. Persistent memory (PM) brings a new opportunity and challenge for tree indexes. Among persistent tree indexes, we find the radix tree is more suitable than B-Tree for the byte-ability of PM. However, the hierarchy of radix remains excessively high, resulting in high read latency. Node splitting imposes a significant overhead on PM. To address these challenges, we propose RWORT, a read and write optimized radix tree for PM. The key focus of RWORT is to minimize random access in PM and provide efficient write operations. RWORT proposed a hierarchical compression mechanism to significantly reduce the tree height. Additionally, RWORT incorporates mini bloom filters to reduce unnecessary access on PM. For efficient write operations, RWORT uses the lazy split flag and the double-linked pointers to reduce the critical path delay. Furthermore, RWORT introduces a low-overhead ring-based bit tree allocator that improves allocation efficiency on PM. Our experiments show that RWORT improves up to 1.62x/4.91x respectively compared to the state-of-the-art radix tree/B-Tree. RWORT also exhibits higher performance in real-world storage systems such as Memcached.