Differentiated storage services

Mesnier, Michael P.; Chen, Feng; Luo, Tian; Akers, Jason

doi:10.1145/2043556.2043563

Cited by 52 publications

(20 citation statements)

References 37 publications

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“…In order to classify the application I/O behaviors, NVMRA should be able to be aware of the application information from I/O requests. Therefore, we modify file system and block device driver to transfer the application information from applications to NVMRA layer [53]. NVMRA can issue read/write operations to the memory device layer, which can control the NVM or flash memory chip.…”

Section: Methodsmentioning

confidence: 99%

NVMRA: utilizing NVM to improve the random write operations for NAND-flash-based mobile devices

Chen

Shen

et al. 2015

Softw. Pract. Exper.

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SUMMARYNAND flash memory has become the major storage media in mobile devices, such as smartphones. However, the random write operations of NAND flash memory heavily affect the I/O performance, thus seriously degrading the application performance in mobile devices. The main reason for slow random write operations is the out-of-place update feature of NAND flash memory. Newly emerged non-volatile memory, such as phase-change memory, spin transfer torque, supports in-place updates and presents much better I/O performance than that of flash memory. All these good features make non-volatile memory (NVM) as a promising solution to improve the random write performance for NAND flash memory. In this paper, we propose a non-volatile memory for random access (NVMRA) scheme to utilize NVM to improve the I/O performance in mobile devices. NVMRA exploits the I/O behaviors of applications to improve the random write performance for each application. Based on different I/O behaviors, such as random write-dominant I/O behavior, NVMRA adopts different storing decisions. The scheme is evaluated on a real Android 4.2 platform. The experimental results show that the proposed scheme can effectively improve the I/O performance and reduce the I/O energy consumption for mobile devices.

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Section: Methodsmentioning

confidence: 99%

NVMRA: utilizing NVM to improve the random write operations for NAND-flash-based mobile devices

Chen

Shen

et al. 2015

Softw. Pract. Exper.

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“…4: Arbitrary sequence of requests over time techniques, however, require hardware and firmware modifications which limits both their applicability and generality. Altering the storage stack to feed semantic information of applications to the caching layer has been examined in [13] to improve the performance of databases. Such technique also suffers from poor generality.…”

Section: Related Workmentioning

confidence: 99%

“…To effectively use SSDs as the OS cache layer, modifications in the conventional caching policies such as Least Recently Used (LRU) and Clock have been suggested by the previous studies [8], [9], [10]. Another suggested approach in the previous work is prioritizing various request types such as filesystem metadata, read, and random requests by using either absolute or relative priority [11], [12], [13], [14]. The absolute priority employed in previous studies is inefficient as observed by a recent study [15].…”

Section: Introductionmentioning

confidence: 99%

ReCA: An Efficient Reconfigurable Cache Architecture for Storage Systems with Online Workload Characterization

Salkhordeh

Ebrahimi

Asadi

2018

IEEE Trans. Parallel Distrib. Syst.

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In recent years, Solid-State Drives (SSDs) have gained tremendous attention in computing and storage systems due to significant performance improvement over Hard Disk Drives (HDDs). The cost per capacity of SSDs, however, prevents them from entirely replacing HDDs in such systems. One approach to effectively take advantage of SSDs is to use them as a caching layer to store performance critical data blocks in order to reduce the number of accesses to HDD-based disk subsystem. Due to characteristics of Flash-based SSDs such as limited write endurance and long latency on write operations, employing caching algorithms at the Operating System (OS) level necessitates to take such characteristics into consideration. Previous OS-level caching techniques are optimized towards only one type of application, which affects both generality and applicability. In addition, they are not adaptive when the workload pattern changes over time. This paper presents an efficient Reconfigurable Cache Architecture (ReCA) for storage systems using a comprehensive workload characterization to find an optimal cache configuration for I/O intensive applications. For this purpose, we first investigate various types of I/O workloads and classify them into five major classes. Based on this characterization, an optimal cache configuration is presented for each class of workloads. Then, using the main features of each class, we continuously monitor the characteristics of an application during system runtime and the cache organization is reconfigured if the application changes from one class to another class of workloads. The cache reconfiguration is done online and workload classes can be extended to emerging I/O workloads in order to maintain its efficiency with the characteristics of I/O requests. Experimental results obtained by implementing ReCA in a 4U rackmount server with SATA 6Gb/s disk interfaces running Linux 3.17.0 show that the proposed architecture improves performance and lifetime up to 24% and 33%, respectively.

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“…Many early systems work on a single-server [7,25,34], or for a specific kind of workloads, such as media recording and streaming [23,46,47] or virtual machines [10].…”

Section: Related Workmentioning

confidence: 99%

ASCAR: Automating contention management for high-performance storage systems

Miller

et al. 2015

2015 31st Symposium on Mass Storage Systems and Technologies (MSST)

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Abstract-High-performance parallel storage systems, such as those used by supercomputers and data centers, can suffer from performance degradation when a large number of clients are contending for limited resources, like bandwidth. These contentions lower the efficiency of the system and cause unwanted speed variances. We present the Automatic Storage Contention Alleviation and Reduction system (ASCAR), a storage traffic management system for improving the bandwidth utilization and fairness of resource allocation. ASCAR regulates I/O traffic from the clients using a rule based algorithm that controls the congestion window and rate limit. The rule-based client controllers are fast responding to burst I/O because no runtime coordination between clients or with a central coordinator is needed; they are also autonomous so the system has no scale-out bottleneck. Finding optimal rules can be a challenging task that requires expertise and numerous experiments. ASCAR includes a SHAred-nothing Rule Producer (SHARP) that produces rules in an unsupervised manner by systematically exploring the solution space of possible rule designs and evaluating the target workload under the candidate rule sets. Evaluation shows that our ASCAR prototype can improve the throughput of all tested workloadssome by as much as 35%. ASCAR improves the throughput of a NASA NPB BTIO checkpoint workload by 33.5% and reduces its speed variance by 55.4% at the same time. The optimization time and controller overhead are unrelated to the scale of the system; thus, it has the potential to support future large-scale systems that can have millions of clients and thousands of servers. As a pure client-side solution, ASCAR needs no change to either the hardware or server software.

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Differentiated storage services

Cited by 52 publications

References 37 publications

NVMRA: utilizing NVM to improve the random write operations for NAND-flash-based mobile devices

NVMRA: utilizing NVM to improve the random write operations for NAND-flash-based mobile devices

ReCA: An Efficient Reconfigurable Cache Architecture for Storage Systems with Online Workload Characterization

ASCAR: Automating contention management for high-performance storage systems

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