SWEEP: Gathering Hot Read/Write Data Together to Minimize Read Reclaims in SSDs

Redundant arrays of independent disk (RAID) is an efficient approach to relieve reliability sacrifice caused by aggressive scale-out of solid state drives (SSDs). Unfortunately, RAID is unfriendly to SSDs due to redundant parity write and data rebuilding. This paper proposes a wear aware RAID design for SSDs, called WARD , which: 1) adaptively organizes RAID stripes according to real-time interblock unbalanced wear for relieving high performance and storage overhead caused by parity data and 2) migrates blocks about to break in advance and leaves these blocks unused to reduce data rebuilding overhead. An efficient block wear detection scheme is employed to detect block wear during the whole lifetime of SSDs. Beginning with a large stripe width RAID instead of the redundant worst-case RAID, WARD reorganizes RAID stripes once wear blocks with high bit error rates come out. WARD divides the original stripe into several short width RAID stripes according to the number of wear blocks and separates all wear blocks into different stripes. This not only reduces parity redundancy but also provides high reliability to avoid more than RAID recoverable error-prone chunks remaining in one stripe. For high wear blocks tending to wear-out, data in them are migrated in advance and then the blocks are left unused, which efficiently avoids performance shock caused by data rebuilding. A reliability model considering interblock unbalanced wear is proposed and reveals that WARD provides a high and stable reliability and greatly prolongs the lifetime of SSDs. Comprehensive experiments based on an SSDsim derivative simulator are carried out and experiment results show that WARD considerably improves system performance compared to the worst-case RAID.

The NVMe Zoned Namespace (ZNS) is a high-performance interface for flash-based solid-state drives (SSDs), which divides the logical address space into fixed-size and sequential-write zones. Meanwhile, ZNS SSDs eliminate in-device garbage collection (GC) by shifting the responsibility of GC to the host. However, the host-side GC of ZNS SSDs is not efficient. On the one hand, data migration during GC first moves data to the host buffer and then writes back the transferred data to the new location in the SSD, resulting in an unnecessary end-to-end transfer overhead. On the other hand, due to the pre-configured mapping between zones and blocks, GC incurs a large block-to-block rewrite overhead, i.e., even if most of the data in a block of the victim zone is valid, the valid data will still be rewritten to another block in the target zone. To address these issues, this article proposes a novel ZNS SSD design that features dynamic zone mapping, termed Brick-ZNS . Brick-ZNS implements two key functionalities: in-storage data migration and address remapping. New ZNS commands are first designed to realize in-storage data migration to avoid the end-to-end transfer overhead of GC while ensuring performance predictability. Then, a remapping strategy exploiting parallel physical blocks is proposed to reduce the large block-to-block rewrite overhead while ensuring zone-level access parallelism. The basic idea of the strategy is to directly remap the parallel physical blocks with a sufficient amount of valid data in the victim zone to the target zone, hence avoiding the large block-to-block rewrite overhead. Based on a full-stack SSD emulator, the evaluation results show that Brick-ZNS improves write throughput by 25% and SSD lifetime by 1.41×.

Nowadays solid state drives (SSDs) are gaining popularity and are replacing magnetic hard disk drives (HDDs) in enterprise storage systems. As a result, extracting the maximum performance from SSDs is becoming crucial to deal with the increasing storage volume and performance needs. Active disks were introduced as a way to offload data-processing tasks from the host into disks freeing system resources and achieving better performance. In this work, we present an active SSD architecture called Self-Sorting SSD that targets to offload sorting operations which are commonly used in data-intensive and database environments and that require heavy data transfer. Processing sorting operations directly on the SSD reduces data transfer from/to the storage devices, increasing system performance and the lifetime of SSDs. Experiments on a real SSD platform reveal that our proposed architecture outperforms traditional external merge sort by up to 60.75%, reduces energy consumption by up to 58.86%, and eliminates all the data transfer overhead to compute sorted results.

Recently, solid state drives (SSDs) are replacing hard disk drives (HDDs) in datacenter storage systems in order to reduce power consumption and improve I/O performance. Additionally, in order to mitigate the performance bottleneck at I/O interface between host and SSD, the PCIe-leveraging NVMe SSD is emerging for datacenter SSDs. The NVMe interface supports the I/O virtualization mechanism called single root I/O virtualization (SR-IOV), which is a device self-virtualization technique for supporting direct paths from virtual machines (VMs) to I/O devices. Multiple virtual machines can share an SR-IOV-supporting physical device without intervention of virtual machine monitor. SR-IOV-supporting SSD should provide a device-level scheduler which can schedule the requests from multiple VMs considering performance isolation and fairness. In this paper, we propose a workload-aware budget compensation scheduling algorithm for the device-level request scheduler. To guarantee the performance isolation, the device-level scheduler estimates the contribution on the garbage collection (GC) cost of each virtual machine in the SSD device. Based on the estimated GC contributions, the budget of each VM is compensated for performance isolation. We experimented the effects of the proposed technique with an SSD simulator. The experiments showed that the scheduler can guarantee the performance isolation when multiple VMs share an NVMe SSD with different workloads.

NAND flash-based high-density solid-state drives (SSDs) commonly have limited write endurance, as a single block becomes unavailable after a finite number of program/erase cycles. The (static) wear leveling algorithms migrate cold data to more worn blocks for yielding an even distribution of wears in SSDs. However, migrating cold data must delay normal I/O processing, and unnecessary wear leveling operations may damage SSD endurance as each operation causes a program/erase cycle. This article proposes an adaptive switch on wear leveling (called ASWL), to boost I/O performance and lifetime for high-density SSDs. The basic idea of ASWL is to adaptively switch the threshold function for intelligently triggering wear leveling operations at different stages of SSD lifetime. To this end, we build a mathematical model determining the wear leveling threshold condition with a dynamic manner, by considering the real-time factors of I/O intensity and the wear difference of SSD blocks. Through a series of emulation experiments on several realistic disk traces, we show that the proposed ASWL mechanism can not only greatly improve I/O performance but also noticeably extend the lifetime of high-density SSDs, in contrast to existing wear leveling methods.

Solid State Drives (SSDs) have replaced magnetic disks in many application areas, as they provide very high performance for arbitrary access patterns. Nevertheless, data written to a physical page has to be erased before a page can be rewritten. The corresponding garbage collection (GC) process can only be performed on a block granularity, where a block includes many pages, impacting both the performance and lifetime of an SSD. The cost of a GC process is typically measured in terms of its write amplification, i.e., the number of blocks internally written by the SSD divided by the number of write requests of the host.Several GC heuristics have been proposed to optimize the write amplification of SSDs. These heuristics have been mostly empirically evaluated, while no thorough theoretical results are available on the optimality of GC algorithms even for seemingly simple cases like uniform and independent access distributions.In this work, we theoretically investigate the GREEDY GC strategy for uniformly independently distributed write accesses. We therefore model the garbage collection process on SSDs as a stochastic process and prove that the expected write amplification incurred by the GREEDY GC strategy is at most that of any other online GC strategy.

Solid state drives (SSDs) are increasingly deployed to construct storage arrays (RAIDs) in enterprise environments. The design decisions in RAID are traditionally devised for HDD RAIDs, which cannot fully exploit the characteristics of SSDs. In particular, SSD lacks the ability to update pages in-place. Random writes in traditional parity-based RAIS (SSD RAID) systems that has static striping result in significantly more writes, degraded performance, and shortened SSD lifetime. By dynamically forming full stripes, log-based design originally proposed in HDD RAID can mitigate the write-hole problem caused by random writes. However, it needs a directory to record locations for all data blocks, resulting in large space overhead and consequently sacrificing addressing efficiency. In this paper, we propose CD-RAIS, a compromise between static striping and dynamic striping. CD-RAIS groups requests that are from different SSD drives and places their corresponding unnecessarily consecutive logical blocks in one stripe. It mitigates the write-hole problem, meanwhile remains the same addressing efficiency as static striping. To enable dynamic data striping fit SSDs, CD-RAIS performs lazy data invalidation and consolidates updates to parity blocks. CD-RAIS greatly alleviates the write request increase due to parity block update in RAIS. Our experimental results show that, for random write dominated workloads, CD-RAIS achieves 65% response time improvement and 31% longer SSD lifespan over traditional RAIS schemes.

While an SSD (Solid State Drive) has been widely used for storage in many computing systems due to its small average latency, how to provide timing guarantees of a delay-sensitive (real-time) task on a real-time system equipped with an SSD has not been fully explored. A recent study has proposed a work-constraining I/O scheduler, called K2, which has succeeded in reducing the tail latency of a real-time task at the expense of compromising the total bandwidth for real-time and non-real-time tasks. Although the queue length bound parameter of the K2 scheduler is a key to regulate the tradeoff between a decrease in the tail latency of a real-time task and an increase in penalty of the total bandwidth, the parameter’s impact on the tradeoff has not been thoroughly investigated. In particular, no studies have addressed how the case of a fully occupied SSD that incurs garbage collection changes the performance of the K2 scheduler in terms of the tail latency of the real-time task and the total bandwidth. In this paper, we systematically analyze the performance of the K2 scheduler for different I/O operation types, based on experiments on Linux. We investigate how the performance is changed on a fully occupied SSD due to garbage collection. Utilizing the investigation, we draw general guidelines on how to select a proper setting of the queue length bound for better performance. Finally, we propose how to apply the guidelines to achieve target objectives that optimize the tail latency of the real-time task and the total bandwidth at the same time, which has not been achieved by previous studies.

Recent advancements in flash-based solid state drive (SSD) make it a highly desirable storage device, especially for data-intensive applications. There are significant more SSDs used in data centers and high performance computing systems. SSDs perform one or two orders better than traditional hard disk drives generally. However, the performance of random writes on SSDs, especially small random writes, is still largely limited due to the garbage collection (GC) process. Existing work tried to utilize the on-device RAM as a write cache to improve the write performance, however directly utilizing it as a normal write cache under utilizes the RAM cache. In this poster, we present our initial study of a cache management scheme that hides the GC latency.

Solid state drives (SSDs) have seen wide deployment in mobiles, desktops,and data centers due to their high I/O performance and low energy consumption. As SSDs write data out-of-place, garbage collection (GC) is required to erase and reclaim space with invalid data. However, GC poses additional writes that hinder the I/O performance, while SSD blocks can only endure a finite number of erasures. Thus, there is a performance-durability tradeoff on the design space of GC. To characterize the optimal tradeoff, this paper formulates an analytical model that explores the full optimal design space of any GC algorithm. We first present a stochastic Markov chain model that captures the I/O dynamics of large-scale SSDs, and adapt the mean-field approach to derive the asymptotic steady-state performance. We further prove the model convergence and generalize the model for all types of workload. Inspired by this model, we propose a randomized greedy algorithm (RGA) that can operate along the optimal tradeoff curve with a tunable parameter. Using trace-driven simulation on DiskSim with SSD add-ons, we demonstrate how RGA can be parameterized to realize the performance-durability tradeoff.

NAND flash memory has been widely adopted as the primary data storage medium in data centers. However, the inherent characteristic of out-of-place updates in NAND flash necessitates garbage collection (GC) operations on NAND flash-based solid-state drives (SSDs), aimed at reclaiming flash blocks occupied by invalid data. GC processes entail additional read and write operations, which can lead to the blocking of user requests, thereby increasing the tail latency. Moreover, frequent execution of GC operations is prone to induce more pages to be written, further reducing the lifetime of SSDs. In light of these challenges, we introduce an innovative GC scheme, termed SplitGC. This scheme leverages the records of data redundancy gathered during periodic read scrub operations within the SSD. By analyzing these features of data duplication, SplitGC enhances the selection strategy for the victim block. Furthermore, it bifurcates the migration of valid data pages into two phases: non-duplicate pages follow standard relocation procedures, whereas the movement of duplicate pages is scheduled during idle periods of the SSD. The experiment results show that our scheme reduces tail latency induced by GC by 8% to 83% at the 99.99th percentile and significantly decreases the amount of valid page migration by 38% to 67% compared with existing schemes.

Random writes significantly limit the application of flash-based Solid State Drive (SSD) in enterprise environment due to its poor latency, negative impact on SSD lifetime and high garbage collection overhead. To release above limitations, we propose a locality-aware cooperative buffer scheme referred to as FlashCoop (Flash Cooperation), which leverages free memory of neighboring storage server to buffer writes over high speed network. Both temporal and sequential localities of access pattern are exploited in the design of cooperative buffer management. Leveraging the filtering effect of the cooperative buffer, FlashCoop can efficiently shape the I/O request stream and improve the sequentiality of the write accesses passed to the SSD. FlashCoop has been extensively evaluated under various enterprise workloads. Our benchmark results conclusively demonstrate that FlashCoop can achieve 52.3% performance improvement and 56.5% garbage collection overhead reduction compared to the system without FlashCoop.

A comprehensive survey of issues in solid state drives

Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs.

Solid State Drives (SSDs) have become popular for mobile devices and PCs nowadays due to their advantages compared with conventional hard disk drives. However, they suffer from endurance problems. An effective approach to increase the endurance of an SSD is to improve the management algorithm of the RAM buffer, a cache-like structure inside the SSD. There are several previous studies of RAM buffer management algorithms which improve the I/O performance and endurance of SSDs by reordering the write operations and exploiting the temporal locality of the write stream. We observe that the sequential write patterns of the write stream can be exploited to reduce the number of expensive erase operations. Using this insight, we propose a new effective method for RAM buffer management algorithms to enhance the endurance of SSDs. We evaluate our proposed method by software simulation and the result shows that the proposed method reduces the number of erasures and average response time compared to the best existing method by up to 17.3% and 16.9% respectively.