Design and Prototype of a Solid-State Cache

Saxena, Mohit; Swift, Michael M.

doi:10.1145/2629491

Cited by 6 publications

(4 citation statements)

References 22 publications

(14 reference statements)

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“…Deduplication and compression can also be employed to extend the SSDs lifetime [36], [37], [38]. Modifying the existing interface between OS and SSDs is also suggested in previous studies to design efficient caching architectures [39], [40]. In [40], a new interface for SSDs is designed, which does not allow overwriting of data pages, to reduce the size of the required DRAM in SSD and also to improve performance.…”

Section: Previous Studiesmentioning

confidence: 99%

An Efficient Hybrid I/O Caching Architecture Using Heterogeneous SSDs

Salkhordeh

Hadizadeh

Asadi

2019

IEEE Trans. Parallel Distrib. Syst.

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Storage subsystem is considered as the performance bottleneck of computer systems in data-intensive applications. Solid-State Drives (SSDs) are emerging storage devices which unlike Hard Disk Drives (HDDs), do not have mechanical parts and therefore, have superior performance compared to HDDs. Due to the high cost of SSDs, entirely replacing HDDs with SSDs is not economically justified. Additionally, SSDs can endure a limited number of writes before failing. To mitigate the shortcomings of SSDs while taking advantage of their high performance, SSD caching is practiced in both academia and industry. Previously proposed caching architectures have only focused on either performance or endurance and neglected to address both parameters in suggested architectures. Moreover, the cost, reliability, and power consumption of such architectures is not evaluated. This paper proposes a hybrid I/O caching architecture that while offers higher performance than previous studies, it also improves power consumption with a similar budget. The proposed architecture uses DRAM, Read-Optimized SSD (RO-SSD), and Write-Optimized SSD (WO-SSD) in a three-level cache hierarchy and tries to efficiently redirect read requests to either DRAM or RO-SSD while sending writes to WO-SSD. To provide high reliability, dirty pages are written to at least two devices which removes any single point of failure. The power consumption is also managed by reducing the number of accesses issued to SSDs. The proposed architecture reconfigures itself between performance-and enduranceoptimized policies based on the workload characteristics to maintain an effective tradeoff between performance and endurance. We have implemented the proposed architecture on a server equipped with industrial SSDs and HDDs. The experimental results show that as compared to state-of-the-art studies, the proposed architecture improves performance and power consumption by an average of 8% and 28%, respectively, and reduces the cost by 5% while increasing the endurance cost by 4.7% and negligible reliability penalty.

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Section: Previous Studiesmentioning

confidence: 99%

An Efficient Hybrid I/O Caching Architecture Using Heterogeneous SSDs

Salkhordeh

Hadizadeh

Asadi

2019

IEEE Trans. Parallel Distrib. Syst.

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“…Modifying the interface between OS and the disk subsystem to further improve the performance is examined in [5]. Such Fig.…”

Section: Related Workmentioning

confidence: 99%

“…To cope with both limited performance of HDDs and data loss concern in the main memory, recent studies have suggested using Solid-State Drives (SSDs) as a caching layer for HDDs [4], [5] (a.k.a., I/O caching shown in Fig. 1).…”

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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“…It is to add SSD to the storage device that consists only of HDD and let SSD play the role of cache unit. However, as HDFS that is used at Hadoop is implemented in JAVA, it causes bottlenecks via JVM (Java Virtual Machine) [4], as a result, performance rises only slightly regardless of the SSD cache [5][6][7]. Therefore, in this paper, we proposed a scheme for loading the block that are being used or about to be used into SSD cache in advance to improve the Read I/O performance significantly in a Hadoop system that uses an SSD as a cache.…”

Section: Introductionmentioning

confidence: 99%

Design and implement of pre-loading SSD cache data using split file on Hadoop MapReduce

Kim

Jeon

2015

Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems

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A storage device that is used mainly in the Hadoop system, HDD, has the slow transfer speed and long latency because of its physical limit. In addition, even though the SSD is the only alternative to traditional HDD, the price of it is too expensive. In order to improve this problem, some distributed file systems use SDD as a cache of HDD. In this paper, in the distributed file system that uses SSD as cache, we propose a mechanism that acquires a block ID that is scheduled to be used or already in use and loads it in SSD cache before the I/O request about the block is made in order to increase the performance of SSD cache during performance of Hadoop MapReduce task. In order to verify the reduction of MapReduce processing time and the improvement of Read I/O performance, which were run based on the proposed mechanism, experiments were carried out via Hadoop Benchmark tool. We could verify through the experiments that the performance of Hadoop MapReduce system was about 11% improved in the system where the proposed mechanism was applied than in the distributed file system where the existing SSD cache was applied. CCS Concepts• Computer systems organization➝Distributed architectures;• Software and its engineering➝Software performance.

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Design and Prototype of a Solid-State Cache

Cited by 6 publications

References 22 publications

An Efficient Hybrid I/O Caching Architecture Using Heterogeneous SSDs

An Efficient Hybrid I/O Caching Architecture Using Heterogeneous SSDs

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

Design and implement of pre-loading SSD cache data using split file on Hadoop MapReduce

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