Figure 2 - uploaded by Abhishek Rajimwale
Content may be subject to copyright.
Write Amplification. In S2 slc , maximum bandwidth is achieved when the write size aligns with the stripe size (1 MB).
Source publication
Solid-state devices (SSDs) have the potential to replace traditional hard disk drives (HDDs) as the de facto storage medium. Unfortunately, there are several decades of spinning-media assumptions embedded in the software stack as an "unwritten contract" [20]. In this paper, we revisit these system-level assumptions in light of SSDs and find that se...
Context in source publication
Similar publications
Coincidence processing in positron emission tomography (PET) is typically done during acquisition of the data. However, on the EXPLORER total-body PET scanner we plan, in addition, to store unpaired single events (i.e. singles) for post-acquisition coincidence processing. A software-based coincidence processor was developed for EXPLORER and its per...
This paper describes trends in the storage technologies associated with Linear Tape Open (LTO) Tape cartridges, hard disk drives (HDD), and NAND Flash based storage devices including solid-state drives (SSD). This technology discussion centers on the relationship between cost/bit and bit density and, specifically on how the Moore’s Law perception t...
Solid-state drives (SSDs) have accelerated the architectural evolution of storage systems with several characteristics (e.g., out-of-place update) compared with hard disk drives (HDD). Out-of-place update of SSDs naturally can support transaction mechanism which is commonly used in systems to provide crash consistency. Thus, transactional functiona...
In this article, we design and implement a cooperative shingle-aware file system, called CosaFS, on heterogeneous storage devices that mix solid-state drives (SSDs) and shingled magnetic recording (SMR) technology to improve the overall performance of storage systems. The basic idea of CosaFS is to classify objects as hot or cold objects based on a...
Solid-State Drives (SSDs) have significant performance advantages over traditional Hard Disk Drives (HDDs) such as lower latency and higher throughput. Significantly higher price per capacity and limited lifetime, however, prevents designers to completely substitute HDDs by SSDs in enterprise storage systems. In this paper, we propose RC-RNN, the f...
Citations
... The system-wide one can also be used in the data servers [23,118,169,187,236] and forwarding layer [2,16,20,159,232] to coordinate accesses and optimize I/O performance of the whole system. ...
The high-performance computing (HPC) I/O stack has been complex due to multiple software layers, the inter-dependencies among these layers, and the different performance tuning options for each layer. In this complex stack, the definition of an “I/O access pattern” has been re-appropriated to describe what an application is doing to write or read data from the perspective of different layers of the stack, often comprising a different set of features. It has become common having to redefine what is meant when discussing a pattern in every new study as no assumption can be made. This survey aims to propose a baseline taxonomy, harnessing the I/O community’s knowledge over the last 20 years. This definition can serve as a common ground for HPC I/O researchers and developers to apply known I/O tuning strategies and design new strategies for improving I/O performance. We seek to summarize and bring a consensus with the multiple ways to describe a pattern based on common features already used by the community over the years.
... To overcome the architectural limitation of the block-based storage model, an object-based storage model is proposed. In this model, the storage management layer is offloaded to the underlying object-based NAND flash device (ONFD) [10]. The ONFD manages data in unit of object instead of logic blocks. ...
Write amplification is a major cause of performance and endurance degradations in NAND flash based storage systems. In an object-based NAND flash device, two causes of write amplification are onode partial update and cascading update. Updating one onode, a kind of small-sized object metadata, invokes partial page update (i.e., onode partial update) that incurs unnecessary migration of the un-updated data. An cascading update denotes that object metadata is updated in a cascading manner due to erase-before-program property of NAND flash memory. In this work, we propose a system design to alleviate onode partial update and cascading update. The proposed system design includes: 1) A multi-level garbage collection technique to minimize unnecessary data migration incurred by onode partial update; 2) A B+ table tree and selective cache design to reduce the write operations associated with cascading update; and 3) A power failure handling technique to guarantee system consistency. Experiment results show that our proposed design can achieve up to 20% write reduction compared to the best state-of-the-art.
... Second, due to the inherent write amplification phenomenon of flash chips, actual write sizes are likely to be much larger than requested ones. Write amplification is triggered by the mismatch of erase and read/write operation units [6] as well as the extra migrations of valid data on to-be-erase blocks. ...
... Second, due to the inherent write amplification phenomenon of flash chips, actual write sizes are likely to be much larger than requested ones. Write amplification is triggered by the mismatch of erase and read/write operation units [6] as well as the extra migrations of valid data on to-beerase blocks. ...
Serving as cache disks, flash-based solid-state drives (SSDs) can significantly boost the performance of read-intensive applications. However, frequent data updating, the necessary condition for classical replacement algorithms (e.g., LRU, MQ, LIRS, and ARC) to achieve a high hit rate, makes SSDs wear out quickly. To address this problem, we propose a new approach—write-efficient caching (WEC)—to greatly improve the write durability of SSD cache. WEC is conducive to reducing the total number of writes issued to SSDs while achieving high hit rates. WEC takes two steps to improve write durability and performance of SSD cache. First, WEC discovers write-efficient data, which tend to be active for a long time period and to be frequently accessed. Second, WEC keeps the write-efficient data in SSDs long enough to avoid excessive number of unnecessary updates. Our findings based on a wide range of popular real-world traces show that write-efficient data does exist in a wide range of popular read-intensive applications. Our experimental results indicate that compared with the classical algorithms, WEC judiciously improves the mean hits of each written block by approximately two orders of magnitude while exhibiting similar or even higher hit rates.
... Unfortunately, most operating system level approaches still use these devices to store files, even if more efficiently [22,23]. However, with removal of the concept of the file all together, this approach will be a significant factor along with further adoption of SSD storage [17]. In fact Seagate has introduced recently an actual network attached object based device called Kinetic Storage [21], which provides a hardware back end for object based databases without any file system protocol access. ...
With the continuously increasing amount of online resources and data such use cases as discovery, maintenance and inter-operation become more and more complex. In particular, data management is becoming one of the main issues with respect to both scientific (large scale simulations or data mining applications) as well as consumer use cases (accessing photos or email attachments on mobile devices). We believe that one of the main bottlenecks blocking development of solutions providing truly seamless developer and user experience is the concept of file and filesystem. We present Filess, vision and architecture of file-less information systems where files are not necessary, neither in the application nor operating system layers.
... Several assumptions about performance from HDDs do not hold when using SSDs and RAID arrays, and different requirements arise [5]. Therefore, we cannot simply classify optimizations by saying they are only suitable for HDDs or SSDs. ...
This work presents the parallel storage device profiling tool SeRRa. Our tool obtains the sequential to random throughput ratio for reads and writes of different sizes on storage devices. In order to provide this information efficiently, SeRRa employs benchmarks to obtain the values for only a subset of the parameter space and estimates the remaining values through linear models. The MPI parallelization of SeRRa presented in this paper allows for faster profiling. Our results show that our parallel SeRRa provides profiles up to 8.7 times faster than the sequential implementation, up to 895 times faster than the originally required time (without SeRRa).
... On some SSDs, there is no difference between sequential and random accesses, but on others, this difference achieves orders of magnitude [3]. The sequential to random throughput ratio on some SSDs surpasses what is observed on some HDDs [4,5]. Therefore, approaches that aim at generating contiguous accesses (originally designed for HDDs) can greatly improve performance when used on SSDs that are also sensitive to access sequentiality. ...
... Their results motivated us to include SJF in our study. Additionally, another reason to include it was because a similar algorithm -Shortest Wait Time First -was reported to present good results as a disk scheduler for SSDs [4]. ...
This article presents our approach to provide input/output (I/O) scheduling with double adaptivity: to applications and devices. In high-performance computing environments, parallel file systems provide a shared storage infrastructure to applications. In the situation where multiple applications access this shared infrastructure concurrently, their performance can be impaired because of interference. Our work focuses on I/O scheduling as a tool to improve performance by alleviating interference effects. The role of the I/O scheduler is to decide the order in which applications' requests must be processed by the parallel file system's servers, applying optimizations to adjust the resulting access pattern for improved performance. Our approach to improve I/O scheduling results is based on using information from applications' access patterns and storage devices' sensitivity to access sequentiality. We have applied machine learning to provide the ability to automatically select the best scheduling algorithm for each situation. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Our results evidence that both aspects – applications and storage devices – are essential to make good scheduling decisions. Copyright
... Nonetheless, since both SSDs and RAID solutions are inherently different from HDDs, they should not be treated simply as "faster disks". Several assumptions about performance from HDDs do not hold when using SSDs and RAID arrays, and different requirements arise [Rajimwale et al. 2009]. ...
... On some SSDs, there is no difference between sequential and random accesses, but on others this difference achieves orders of magnitude [Chen et al. 2009]. The sequential to random throughput ratio on some SSDs surpasses what is observed on some HDDs [Rajimwale et al. 2009]. ...
... With the growing adoption of solid state drives, several works focused at characterizing these devices by evaluating their performance over several access patterns [Chen et al. 2009, Rajimwale et al. 2009. These works point at SSDs' project options, their impact on performance, and illustrate common phenomena as write amplification and stripe alignment. ...
This work presents the parallel storage device profiling tool SeRRa. Our tool obtains the sequential to random throughput ratio for reads and writes of different sizes on storage devices. In order to provide this information efficiently, SeRRa employs benchmarks to obtain the values for only a subset of the parameter space and estimates the remaining values through linear models. The MPI parallelization of SeRRa presented in this paper allows for faster profiling. Our results show that our parallel SeRRa provides profiles up to 8:7 times faster than the sequential implementation, up to 895 times faster than the originally required time (without SeRRa).
... -Reduced Parallelism. While striping and interleaving can improve performance for sequential writes, its ability to deal with random write is very limited Rajimwale et al. 2009]. ...
Existing space management and address mapping schemes for flash-based Solid-State-Drive (SSD) operate either at page or block granularity, with inevitable limitations in terms of memory requirement, performance, garbage collection, and scalability. To overcome these limitations, we proposed a novel space management and address mapping scheme for flash referred to as Z-MAP, which manages flash space at granularity of Zone. Each Zone consists of multiple numbers of flash blocks. Leveraging workload classification, Z-MAP explores Page-mapping Zone (Page Zone) to store random data and handle a large number of partial updates, and Block-mapping Zone (Block Zone) to store sequential data and lower the overall mapping table. Zones are dynamically allocated and a mapping scheme for a Zone is determined only when it is allocated. Z-MAP uses a small part of Flash memory or phase change memory as a streaming Buffer Zone to log data sequentially and migrate data into Page Zone or Block Zone based on workload classification. A two-level address mapping is designed to reduce the overall mapping table and address translation latency. Z-MAP classifies data before it is permanently stored into Flash memory so that different workloads can be isolated and garbage collection overhead can be minimized.
Z-MAP has been extensively evaluated by trace-driven simulation and a prototype implementation on OpenSSD. Our benchmark results conclusively demonstrate that Z-MAP can achieve up to 76% performance improvement, 81% mapping table reduction, and 88% garbage collection overhead reduction compared to existing Flash Translation Layer (FTL) schemes.
... In addition, each block can be erased only a finite number of times. A typical MLC flash memory has around 10,000 erase cycles, while a SLC flash memory has around 100,000 erase cycles [11,13]. ...
... -Reduced Parallelism. While striping and interleaving can improve performance for sequential writes, its ability to deal with random write is very limited [13]. ...
... Four buffer schemes are implemented: 1) our CBM, 2) BPLRU [14], 3) FAB [20], and BPAC [26]. We use FAST as FTL and allocate 3% of the total flash memory as log-blocks [13]. We use DRAM is read cache and STT-MRAM as write buffer. ...
Random writes significantly limit the application of Solid State Drive (SSD) in the I/O intensive applications such as scientific computing, Web services, and database. While several buffer management algorithms are proposed to reduce random writes, their ability to deal with workloads mixed with sequential and random accesses is limited. In this paper, we propose a cooperative buffer management scheme referred to as CBM, which coordinates write buffer and read cache to fully exploit temporal and spatial localities among I/O intensive workload. To improve both buffer hit rate and destage sequentiality, CBM divides write buffer space into Page Region and Block Region. Randomly written data is put in the Page Region at page granularity, while sequentially written data is stored in the Block Region at block granularity. CBM leverages threshold-based migration to dynamically classify random write from sequential writes. When a block is evicted from write buffer, CBM merges the dirty pages in write buffer and the clean pages in read cache belonging to the evicted block to maximize the possibility of forming full block write. CBM has been extensively evaluated with simulation and real implementation on OpenSSD. Our testing results conclusively demonstrate that CBM can achieve up to 84% performance improvement and 85% garbage collection overhead reduction compared to existing buffer management schemes.
