A semi-preemptive garbage collector for solid state drives

Lee, Jung‐Hee; Kim, Young-Jae; Shipman, Galen; Oral, Sarp; Wang, Feiyi; Kim, Jong Man

doi:10.1109/ispass.2011.5762711

Cited by 57 publications

(17 citation statements)

References 26 publications

(18 reference statements)

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“…Because of its importance, extensive research has been reported in the literature to reduce the negative impact of GC in flash memory SSDs [14,20,34] and to cluster streaming data for data mining [2,7]. For a comprehensive review, readers are referred to [11,29,36].…”

Section: Related Workmentioning

confidence: 99%

Warcip

Yang¹,

Pei²,

Yang³

2019

Proceedings of the 12th ACM International Conference on Systems and Storage

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The storage volume of SSDs has been greatly increased recently with emerging multi-layer 3D triple-level cell and quad-level cell. However, one critical overhead of any flash memory SSD is the garbage collection (GC) process that is necessary due to the inherent physical property of flash memories. GC is a time consuming process that slows down I/O performance and decreases endurance of SSD. To minimize the negative impact of GC, we introduce Write Amplification Reduction by Clustering I/O Pages (WARCIP). The idea is to use a clustering algorithm to minimize the rewrite interval variance of pages in a flash block. As a result, pages in a flash block tend to have a similar lifetime, minimizing write amplification during a garbage collection. We have implemented WARCIP on an enterprise NVMe SSD. Both simulation and measurement experiments have been carried out. Real world I/O traces and standard I/O benchmarks are used in our experiments to assess the potential benefit of WARCIP. Experiment results show that WARCIP reduces write amplification dramatically and the number of block erasures by 4.45 times on average, implying extended lifetimes of flash SSDs.

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Section: Related Workmentioning

confidence: 99%

Warcip

Yang¹,

Pei²,

Yang³

2019

Proceedings of the 12th ACM International Conference on Systems and Storage

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“…Recently, some research introduced a GC selection algorithm designed for caching [28,12]. While reducing GC overhead for flash-based devices used in different applications has been previously discussed [20,18], there has been a lack of research on coordinating GC with different cache components.…”

Section: Related Workmentioning

confidence: 99%

Hec

Yang

Plasson

Gillis

et al. 2013

Proceedings of the 6th International Systems and Storage Conference on - SYSTOR '13

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Flash memory is widely used for its fast random I/O access performance in a gamut of enterprise storage applications. However, due to the limited endurance and asymmetric write performance of flash memory, minimizing writes to a flash device is critical for both performance and endurance. Previous studies have focused on flash memory as a candidate for primary storage devices; little is known about its behavior as a Solid State Cache (SSC) device. In this paper, we propose HEC, a High Endurance Cache that aims to improve overall device endurance via reduced media writes and erases while maximizing cache hit rate performance. We analyze the added write pressures that cache workloads place on flash devices and propose optimizations at both the cache and flash management layers to improve endurance while maintaining or increasing cache hit rate. We demonstrate the individual and cumulative contributions of cache admission policy, cache eviction policy, flash garbage collection policy, and flash device configuration on a) hit rate, b) overall writes, and c) erases as seen by the SSC device. Through our improved cache and flash optimizations, 83% of the analyzed workload ensembles achieved increased or maintained hit rate with write reductions up to 20x, and erase count reductions up to 6x.

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“…We have used the Microsoft Research SSD simulator [4], which is based on DiskSim [8] and has been used in several other studies [25], [26], [44]. We have simulated a NAND flash SSD, with the parameters described in Table IV.…”

Section: B Simulator Implementation and Setupmentioning

confidence: 99%

Active Flash: Out-of-core data analytics on flash storage

Boboila

Kim

Vazhkudai

et al. 2012

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)

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Abstract-Next generation science will increasingly come to rely on the ability to perform efficient, on-the-fly analytics of data generated by high-performance computing (HPC) simulations, modeling complex physical phenomena. Scientific computing workflows are stymied by the traditional chaining of simulation and data analysis, creating multiple rounds of redundant reads and writes to the storage system, which grows in cost with the ever-increasing gap between compute and storage speeds in HPC clusters. Recent HPC acquisitions have introduced compute nodelocal flash storage as a means to alleviate this I/O bottleneck.We propose a novel approach, Active Flash, to expedite data analysis pipelines by migrating to the location of the data, the flash device itself. We argue that Active Flash has the potential to enable true out-of-core data analytics by freeing up both the compute core and the associated main memory. By performing analysis locally, dependence on limited bandwidth to a central storage system is reduced, while allowing this analysis to proceed in parallel with the main application. In addition, offloading work from the host to the more power-efficient controller reduces peak system power usage, which is already in the megawatt range and poses a major barrier to HPC system scalability.We propose an architecture for Active Flash, explore energy and performance trade-offs in moving computation from host to storage, demonstrate the ability of appropriate embedded controllers to perform data analysis and reduction tasks at speeds sufficient for this application, and present a simulation study of Active Flash scheduling policies. These results show the viability of the Active Flash model, and its capability to potentially have a transformative impact on scientific data analysis.

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A semi-preemptive garbage collector for solid state drives

Cited by 57 publications

References 26 publications

Warcip

Warcip

Hec

Active Flash: Out-of-core data analytics on flash storage

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