Write Amplification Factor Research Articles

SolsDB: Solve the Ethereum’s bottleneck caused by storage engine

Ethereum’s block synchronization process interacts with the storage engine in two parts: waiting for state data to be read while processing transactions and waiting to write data after processing is complete. Presently, Ethereum utilizes the LSM-tree-based storage engine that not only falls short of meeting Ethereum’s read performance requirements but also presents additional mismatches. In this paper, we propose the single-level ordered log structure database (SolsDB), which fully utilizes state data features to satisfy the workload requirements of Ethereum block synchronization process and suggest novel interactions to deliver state data features to the storage engine. The write feature of state data is utilized to store state data in blocks and the file is globally ordered, which avoids write amplification caused by compaction. By delivering the read feature, the Parser module is crafted to parse the query key, enabling direct file location without the need for level-by-level querying, thus addressing the issue of read amplification. Extensive experiments are conducted to validate the performance of SolsDB. Compared with LevelDB, SolsDB improves read performance by up to 4.7x, reduces read tail latency by 68.7% to 83.3%, and reduces the write amplification factor by 49.1% to 76.1%.

FlashAlloc: Dedicating Flash Blocks by Objects

For a write request, today's flash storage cannot distinguish the logical object it comes from ( e.g. , SSTables in RocksDB). In such object-oblivious flash devices, concurrent writes from different objects are simply packed in their arrival order to flash memory blocks; hence data pages from multiple objects with different lifetimes are multiplexed onto the same flash blocks. This multiplexing incurs write amplification, worsening the performance. Tackling the multiplexing problem, we propose a novel interface for flash storage, FlashAlloc. It is used to pass the logical address ranges of objects to the underlying flash device and thus to enlighten the device to stream writes by objects. The object-aware flash storage can now de-multiplex concurrent writes from multiple objects with distinct deathtimes into per-object dedicated flash blocks. In essence, the interface enables the per-object fine-grained write streaming. Given that popular data stores tend to separate writes by logical objects, we can achieve, compared to the existing solutions, transparent streaming just by calling FlashAlloc upon object creation. Also, FlashAlloc is adaptive to workload changes, and liberates the stream conflicts in the multi-tenant environment. Our experimental results using an open-source SSD prototype demonstrate that FlashAlloc can reduce the device-level write amplification factor (WAF) under RocksDB, F2FS, and MySQL by 1.5, 2.5, and 0.3, respectively and improve their throughput by 2.7x, 1.8x, and 1.2x, respectively. Also, FlashAlloc can mitigate the WAF interference among tenants: when running RocksDB and MySQL together on the same SSD, FlashAlloc reduced WAF from 2.5 to 1.6 and doubled their throughputs.

A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks

To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.

On-Demand Garbage Collection Algorithm with Prioritized Victim Blocks for SSDs

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.

Automatic Stream Identification to Improve Flash Endurance in Data Centers

The demand for high performance I/O in Storage-as-a-Service (SaaS) is increasing day by day. To address this demand, NAND Flash-based Solid-state Drives (SSDs) are commonly used in data centers as cache- or top-tiers in the storage rack ascribe to their superior performance compared to traditional hard disk drives (HDDs). Meanwhile, with the capital expenditure of SSDs declining and the storage capacity of SSDs increasing, all-flash data centers are evolving to serve cloud services better than SSD-HDD hybrid data centers. During this transition, the biggest challenge is how to reduce the Write Amplification Factor (WAF) as well as to improve the endurance of SSD since this device has a limited program/erase cycles. A specified case is that storing data with different lifetimes (i.e., I/O streams with similar temporal fetching patterns such as reaccess frequency) in one single SSD can cause high WAF, reduce the endurance, and downgrade the performance of SSDs. Motivated by this, multi-stream SSDs have been developed to enable data with a different lifetime to be stored in different SSD regions. The logic behind this is to reduce the internal movement of data—when garbage collection is triggered, there are high chances of having data blocks with either all the pages being invalid or valid. However, the limitation of this technology is that the system needs to manually assign the same streamID to data with a similar lifetime. Unfortunately, when data arrives, it is not known how important this data is and how long this data will stay unmodified. Moreover, according to our observation, with different definitions of a lifetime (i.e., different calculation formulas based on selected features previously exhibited by data, such as sequentiality, and frequency), streamID identification may have varying impacts on the final WAF of multi-stream SSDs. Thus, in this article, we first develop a portable and adaptable framework to study the impacts of different workload features and their combinations on write amplification. We then propose a feature-based stream identification approach, which automatically co-relates the measurable workload attributes (such as I/O size, I/O rate, and so on.) with high-level workload features (such as frequency, sequentiality, and so on.) and determines a right combination of workload features for assigning streamIDs . Finally, we develop an adaptable stream assignment technique to assign streamID for changing workloads dynamically. Our evaluation results show that our automation approach of stream detection and separation can effectively reduce the WAF by using appropriate features for stream assignment with minimal implementation overhead.

Gather Interface for Freezing Pages in Flash Storage

It is usual in many applications that a large fraction of data pages is, once created, actively updated for a while, and then eventually no longer updated. In flash storage, these frozen pages tend to spread over physical flash blocks and thus are repeatedly relocated upon garbage collection, exacerbating the write amplification. To address the problem of scattered frozen pages, we propose the interface, which allows users or applications to inform the flash storage of frozen pages explicitly. Upon receiving the call, FTL clusters and isolates frozen pages into a physically separate space so that they are no longer relocated upon garbage collection. Experimental results confirm that can reduce the write amplification factor from 25% to 40% of the conventional SSDs. The scheme is novel in the way it opens up a model case of physical tuning knobs in flash storage.

Analysis and Optimization of Persistent Memory Index Structures’ Write Amplification

Using persistent memory (PM) for an in-memory database (IMDB) system significantly extends data processing capacity and reduces service downtime for the recovery process in case of system failure. However, it requires an index structure tailored for the characteristics of PM to recover the consistent state from system failure. The processor flushes data with the cache line size to the memory controller, whereas the PM media writes with the media-write unit size, which is four times larger than the cache line size. This mismatch in write sizes amplifies the quantity of data written by an index structure at the physical media level. Additionally, the PM-aware index structures explicitly and frequently flush cache lines to ensure memory write orders, which worsen the write amplification factor. The write amplification not only degrades the performance but also shortens the lifespan of PM media. This study analyzes the write amplification and performance of various PM-aware index structures at the memory controller and physical media levels. Furthermore, this paper proposes four optimization techniques based on our observation: removal of unintended bloat, cache flush coalescing, early eviction of volatile data, and XPLine-aware unit sizing. Our analysis demonstrated that up to 3.9 times as many writes could occur depending on the choice of the index structure. We also revealed that the proposed optimization techniques could save up to 51% of physical media write operations and improve throughput by up to 63%.

An empirical study of I/O separation for burst buffers in HPC systems

To meet the exascale I/O requirements for the High-Performance Computing (HPC), a new I/O subsystem, Burst Buffer, based on solid state drives (SSD), has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation that requires costly garbage collection (GC) and increases the number of bytes written to the SSD. To address this data fragmentation challenge, a new multi-stream feature has been developed for SSDs. In this work, we develop an I/O Separation scheme called BIOS to leverage this multi-stream feature to group the I/O streams based on the user IDs. We propose a stream-aware scheduling policy based on burst buffer pools in the workload manager, and integrate the BIOS with the workload manager to optimize the I/O separation scheme in burst buffer. We evaluate the proposed framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. Experimental results show that the BIOS could improve the performance by 1.44x on average and reduce the Write Amplification Factor (WAF) by up to 1.20x. These demonstrate the potential benefits of the I/O separation scheme for solid state storage systems.

Probability-Based Address Translationfor Flash SSDs

Thanks to the advance of NAND scaling technologies, an ultra-scale SSD (e.g., $>$ > 100 TB) is introduced to markets. This rapid increase of SSD capacity, however, comes at the cost of more DRAM which resides in an SSD controller for logical-to-physical (L2P) address translation. Many have proposed various address translation algorithms to reduce DRAM, but they fail to provide short read latency, in particular when a workload has weak locality. This letter proposes a novel probability-based address translation algorithm, called ProbFTL . In contrast to existing translation techniques that maintain exact L2P mapping, ProbFTL employs a probability-based data structure, a bloom filter, for address translation. By leveraging a space-efficient nature of a bloom filter, ProbFTL reduces the amount of DRAM for address translation to 20 percent of the existing techniques. The read latency of ProbFTL is not affected from locality of a workload; ProbFTL guarantees a read amplification factor of 1.1 even under a random read workload. ProbFTL exhibits slightly worse garbage collection efficiency, but its write amplification factor is maintained sufficiently low.

Enhancing eMMC using Multi-Stream technique

Multi-stream for SSD is a concept where the host writes data with similar expected lifetimes to contiguous blocks of NAND memory. Consequently, this data has higher chances of being invalidated together. During garbage collection (GC), there will be minimal valid pages to copy, resulting in improved endurance and decreased write-amplification factor. Implementing Multi-stream in eMMC devices presents challenges due to lower DRAM, lower computational resource available. Maintenance of stream related information requires increase in DRAM usage, transfer buffer usage as well as addition computation for GC of stream related memory area. In this paper, we examine implementation of Multi-Stream concept in eMMC despite its low resource constraints. We have experimented with an implementation that supports up to 4 streams which uses additional ~108 bytes of DRAM and ~104 bytes of code section, and improves WAF by ~50% on FIO (File Input/Output) benchmarking setup where lifetimes are simulated by multiple instances of FIO.

GCMix

In multi-level cell (MLC) NAND flash memory, two logical pages are overlapped on a single physical page. Even after a logical page is programmed, the data can be corrupted if the programming of the coexisting logical page is interrupted. This phenomenon is called paired page interference. This article proposes a novel software technique to deal with the paired page interference without any additional hardware or extra page write. The proposed technique utilizes valid pages in the victim block during garbage collection (GC) as the backup against the interference, and pairs them with incoming pages written by the host. This approach eliminates undesirable page copy to backup pages against the interference. However, such a strategy has an adverse effect on the hot/cold separation policy, which is essential to improve the efficiency of GC. To limit the downside, we devise a metric to estimate the benefit of GCMix on-the-fly so that GCMix can be adaptively utilized only when the benefit outweighs the overhead. Evaluations using synthetic and real workloads show GCMix can effectively deal with the paired page interference, reducing the write amplification factor by up to 17.5%compared to the traditional technique, while providing comparable I/O performance.

Exploiting Compression-Induced Internal Fragmentation for Power-Off Recovery in SSD

Recovery from sudden power-off (SPO) is one of the primary concerns among practitioners which bars the quick and wide deployment of flash storage devices. In this work, we propose Metadata Embedded Write (MEW), a novel scheme for handling the sudden power-off recovery in modern flash storage devices. Given that a large fraction of commercial SSDs employ compression technology, MEW exploits the compression-induced internal fragmentation in the data area to store rich metadata for fast and complete recovery. MEW consists of (i) a metadata embedding scheme to harbor SSD metadata in a physical page together with multiple compressed logical pages, (ii) an allocation chain based fast recovery scheme, and (iii) a light-weight metadata logging scheme which enables MEW to maintain the metadata for incompressible data, too. We performed extensive experiments to examine the performance of MEW. The performance overhead of MEW is 3 percent in the worst case, in terms of the write amplification factor, compared to the pure compression-based FTL that does not have any recovery scheme.

SSD 수명 관점에서 리눅스 I/O 스택에 대한 실험적 분석

낸드 플래시 기반의 SSD (Solid-State Drive)는 HDD (Hard Disk Drive) 대비 월등한 성능에도 불구하고 쓰기 회수 제한이라는 태생적 단점을 가지고 있다. 이로 인해 SSD의 수명은 워크로드에 의해 결정되어 SSD의 기술 변화 추세인 SLC (Single Level Cell) 에서 MLC (Multi Level Cell) 로의 전환, MLC에서 TLC (Triple Level Cell) 로의 전환에 있어 큰 도전이 될 수 있다. 기존 연구들은 주로 wear-leveling 또는 하드웨어 아키텍처 측면에서 SSD의 수명 개선을 다루었으나, 본 논문에서는 호스트가 요청한 쓰기에 대해 SSD가 낸드플래시 메모리를 통해 처리하는 수명관점의 효율성을 대변하는 WAF (Write Amplification Factor) 관점에서 Host I/O 스택 중 파일 시스템, I/O 스케줄러, 링크 전력에 대해 JEDEC 엔터프라이즈 워크로드를 이용해 I/O 스택 최적 구성에 대해 실험적 분석을 수행하였다. WAF는 SSD의 FTL의 효율성을 측정하는 지표로 수명관점에서 가장 객관적으로 사용한다. I/O 스택에 대한 수명 관점의 최적 구성은 MinPower-Dead-XFS로 최대 성능 조합인 MaxPower-Cfq-Ext4에 비해 성능은 13% 감소하였지만 수명은 2.6 배 연장됨을 확인하였다. 이는 I/O 스택의 최적화 구성에 있어, SSD 성능 관점뿐만 아니라 수명 관점의 고려에 대한 유의미를 입증한다.

ZFTL: power-efficient data compression support for NAND flash-based consumer electronics devices

Flash translation layers play an important role in determining the storage performance and lifetime of NAND flash-based consumer electronics devices. In this paper, we present a flash translation layer called zFTL, which reduces the amount of data written to NAND flash memory by supporting on-line, transparent data compression based on the X-Match algorithm. To minimize compression overhead and power consumption, we also propose a novel prediction scheme that identifies incompressible data in advance without going through full compression. Our evaluations with five real-world workloads show that zFTL successfully enhances storage performance and lifetime by improving the write amplification factor (WAF) by a factor of 2.6 (geometric mean) compared to the case without compression support. In addition, we find that the proposed prediction scheme is effective in reducing power consumption by skipping compression for incompressible data.