Modern NVMe SSDs are widely deployed in diverse domains due to characteristics like high performance, robustness, and energy efficiency. It has been observed that the impact of interference among the concurrently running workloads on their overall response time differs significantly in these devices, which leads to unfairness. Workload intensity is a dominant factor influencing the interference. Prior works use a threshold value to characterize a workload as high-intensity or low-intensity; this type of characterization has drawbacks due to lack of information about the degree of low- or high-intensity. A data cache in an SSD controller - usually based on DRAMs - plays a crucial role in improving device throughput and lifetime. However, the degree of parallelism is limited at this level compared to the SSD back-end consisting of several channels, chips, and planes. Therefore, the impact of interference can be more pronounced at the data cache level. No prior work has addressed the fairness issue at the data cache level to the best of our knowledge. In this work, we address this issue by proposing a fuzzy logic-based fairness control mechanism. A fuzzy fairness controller characterizes the degree of flow intensity (i.e., the rate at which requests are generated) of a workload and assigns priorities to the workloads. We implement the proposed mechanism in the MQSim framework and observe that our technique improves the fairness, weighted speedup, and harmonic speedup of SSD by 29.84%, 11.24%, and 24.90% on average over state of the art, respectively. The peak gains in fairness, weighted speedup, and harmonic speedup are 2.02x, 29.44%, and 56.30%, respectively.

Fuzzy fairness controller for NVMe SSDs

The NVMe Zoned Namespace (ZNS) is a new type of storage interface, which divides logical address space into fixed-size zones, and each zone strictly follows a sequential write constraint with a write pointer. Owing to the sequential write constraint of the ZNS, I/O requests would not be scheduled arbitrarily like the traditional SSDs with block interface. When multiple applications concurrently access one ZNS SSD hardware, the constraint deteriorates I/O blocking and causes huge unfairness. To resolve the problem, we propose a self-balance I/O scheduling dedicated for ZNS SSDs, called Fair-ZNS, to balance the slowdown among multiple applications and ensure fairness. Fair-ZNS identifies the unfair requests by the maximum slowdown value, and violently schedules these requests into the head of the queues overcoming the sequential write constraint. To eliminate the negative effect of the violent scheduling, Fair-ZNS deploys a self-balancing coordinator to fine-tune the order of the requests. Comprehensive evaluations show that Fair-ZNS alleviates I/O blocking and reduces average waiting time by 8.3×, increases fairness by 2.3×, and decreases the max slowdown by 5.1× averagely when compared to the current ZNS SSDs. 

Fair-ZNS: Enhancing Fairness in ZNS SSDs through Self-balancing I/O Scheduling

Modern NVMe SSDs have been widely deployed in multi-tenant cloud computing environments or multi-programming systems. When multiple applications concurrently access one SSD hardware, unfairness within the shared SSD will slow down the application significantly and lead to a violation of service level objectives. However, traditional data cache management within SSDs mainly focuses on improving cache hit ratio, which causes data cache contention and sacrifices fairness among multiple applications. In this paper, we propose a DRAM-based Over-Provisioning (OP) cache management mechanism, named Justitia, to reduce data cache contention and improve fairness for modern SSDs. Justitia consists of two stages including Static-OP stage and Dynamic-OP stage. Through the novel OP mechanism in the two stages, Justitia reduces the max slowdown by <inline-formula><tex-math notation="LaTeX">$4.5\times$</tex-math></inline-formula> on average. At the same time, Justitia increases fairness by <inline-formula><tex-math notation="LaTeX">$20.6\times$</tex-math></inline-formula> and buffer hit ratio by <inline-formula><tex-math notation="LaTeX">$19.6\%$</tex-math></inline-formula> averagely, compared with the traditional shared mechanism.

Improving Fairness for SSD Devices through DRAM Over-Provisioning Cache Management

Mobile devices today often have multiple applications running simultaneously in the background. These background applications could rapidly consume storage cache resources, thus degrading the performance of foreground applications as well as the user experience. This issue could get worse as modern mobile devices are employing universal flash storage (UFS), which supports faster transmission speed and full-duplex transmission. In this article, a foreground application-aware cache management approach, FOAM, is proposed to address this issue. Through adaptive management of storage cache resources with the awareness of I/O workload patterns, UFS device features, and foreground/background information, I/O performance of foreground application is significantly improved. Experimental results show that the proposed approach could boost the performance of foreground read I/O by 45.9%, foreground write I/O by 18.4% on average compared with the existing approach.

Boosting User Experience via Foreground-Aware Cache Management in UFS Mobile Devices

When processing I/O requests, the current Linux kernel does not adequately consider the urgency of user-centric tasks closely related to user experience. To solve this critical problem, we developed a practical method in this study to enhance user experience in a computing environment wherein non-volatile memory express (NVMe) solid-state drives (SSDs) serve as storage devices. In our proposed scheme, I/O requests that originate from the user-centric tasks were preferentially served across various levels of queues by modifying the multi-queue block I/O layer of the Linux kernel, considering the dispatch method of NVMe SSDs. Our scheme tries to give as fast a path as possible for I/O requests from user-centric tasks among many queues with different levels. Especially, when the SSD is overburdened, it avoids the queues with many pending I/O requests and thus can significantly reduce the I/O latency of user-centric tasks. We implemented our proposed scheme in the Linux kernel and performed practical evaluations on a commercial SSD. The experimental results showed that the proposed scheme achieved significant enhancement in the launch time of five widely used applications by up to ~65%.

Practical Enhancement of User Experience in NVMe SSDs

NVMe SSDs are nowadays widely deployed in various computing platforms due to its high performance and low power consumption, especially in data centers to support modern latency-sensitive applications. NVMe SSDs improve on SATA and SAS interfaced SSDs by providing a large number of device I/O queues at the host side and applications can directly manage the queues to concurrently issue requests to the device. However, the currently deployed request scheduling approach is oblivious to the states of the various device internal components and thus may lead to suboptimal decisions due to various resource contentions at different layers inside the SSD device. In this work, we propose a Conflict Aware Request Scheduling policy named CARS for NVMe SSDs to maximally leverage the rich parallelism available in modern NVMe SSDs. The central idea is to check possible conflicts that a fetched request might be associated with before dispatching that request. If there exists a conflict, it refrains from issuing the request and move to check a request in the next submission queue. In doing so, our scheduler can evenly distribute the requests among the parallel idle components in the flash chips, improving performance. Our evaluations have shown that our scheduler can reduce the slowdown metric by up to 46% relative to the de facto round-robin scheduling policy for a variety of patterned workloads.

CARS: A Multi-layer Conflict-Aware Request Scheduler for NVMe SSDs

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CARS: A Multi-layer Conflict-Aware Request Scheduler for NVMe SSDs