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.