Emulating a Shingled Write Disk

Pitchumani, Rekha; Hospodor, Andy; Amer, Ahmed; Kang, Yangwook; Miller, Ethan L.; Long, Darrell D. E.

doi:10.1109/mascots.2012.46

Cited by 18 publications

(5 citation statements)

References 8 publications

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“…There are previous works that exploit seek time of disk arm to determine the sector layout strategy in HDDs [11], [16], [17]. However, the HDDs and SSDs have a different physical structure.…”

Section: Related Workmentioning

confidence: 99%

Power Consumption Signature: Characterizing an SSD

Yoo

Lee

Won

2016

IEICE Trans. Inf. & Syst.

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SUMMARYSSDs consist of non-mechanical components (host interface, control core, DRAM, flash memory, etc.) whose integrated behavior is not well-known. This makes an SSD seem like a black-box to users. We analyzed power consumption of four SSDs with standard I/O operations. We find the following: (a) the power consumption of SSDs is not significantly lower than that of HDDs, (b) all SSDs we tested had similar power consumption patterns which, we assume, is a result of their internal parallelism. SSDs have a parallel architecture that connects flash memories by channel or by way. This parallel architecture improves performance of SSDs if the information is known to the file system. This paper proposes three SSD characterization algorithms to infer the characteristics of SSD, such as internal parallelism, I/O unit, and page allocation scheme, by measuring its power consumption with various sized workloads. These algorithms are applied to four real SSDs to find: (i) the internal parallelism to decide whether to perform I/Os in a concurrent or an interleaved manner, (ii) the I/O unit size that determines the maximum size that can be assigned to a flash memory, and (iii) a page allocation method to map the logical address of write operations, which are requested from the host to the physical address of flash memory. We developed a data sampling method to provide consistency in collecting power consumption patterns of each SSD. When we applied three algorithms to four real SSDs, we found flash memory configurations, I/O unit sizes, and page allocation schemes. We show that the performance of SSD can be improved by aligning the record size of file system with I/O unit of SSD, which we found by using our algorithm. We found that Q Pro has I/O unit of 32 KB, and by aligning the file system record size to 32 KB, the performance increased by 201% and energy consumption decreased by 85%, which compared to the record size of 4 KB.

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Section: Related Workmentioning

confidence: 99%

Power Consumption Signature: Characterizing an SSD

Yoo

Lee

Won

2016

IEICE Trans. Inf. & Syst.

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“…[19] designed the SMR simulation platform for various testing and analysis of shingled translation layer designs. Pitchumani et al [16] developed a tool to imitate SMR disk by implementing a shingled device-mapper target on top of the Linux kernel's block devices. Their goal is to allow STL to be evaluated on actual hard drives.…”

Section: Related Workmentioning

confidence: 99%

IMRSim: A Disk Simulator for Interlaced Magnetic Recording Technology

Zeng¹,

Chen²,

Yang³

et al. 2022

Preprint

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The emerging interlaced magnetic recording (IMR) technology achieves a higher areal density for hard disk drive (HDD) over the conventional magnetic recording (CMR) technology. IMR-based HDD interlaces top tracks and bottom tracks, where each bottom track is overlapped with two neighboring top tracks. Thus, top tracks can be updated without restraint, whereas bottom tracks can be updated by the time-consuming read-modify-write (RMW) or other novel update strategy. Therefore, the layout of the tracks between the IMR-based HDD and the CMR-based HDD is much different. Unfortunately, there has been no related disk simulator and product available to the public, which motivates us to develop an open-source IMR disk simulator to provide a platform for further research.We implement the first public IMR disk simulator, called IMRSim, as a block device driver in the Linux kernel, simulating the interlaced tracks and implementing many stateof-the-art data placement strategies. IMRSim is built on the actual CMR-based HDD to precisely simulate the I/O performance of IMR drives. While I/O operations in CMR-based HDD are easy to visualize, update strategy and multi-stage allocation strategy in IMR are inherently dynamic. Therefore, we further graphically demonstrate how IMRSim processes I/O requests in the visualization mode. We release IMRSim as an open-source IMR disk simulation tool and hope to attract more scholars into related research on IMR technology.

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“…Pitchumani et al emulated a shingled write disk on a traditional hard drive. While informative, it is not necessary to fully emulate a shingled write disk to measure the reduction in data movement [Pitchumani et al 2012]. Skylight [Aghayev and Desnoyers 2015] was novel and drilled a hole into a SMR drive to understand how Seagate SMR drives work.…”

Section: Shingled Diskmentioning

confidence: 99%

Classifying Data to Reduce Long-Term Data Movement in Shingled Write Disks

et al. 2016

Self Cite

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Shingled magnetic recording (SMR) is a means of increasing the density of hard drives that brings a new set of challenges. Due to the nature of SMR disks, updating in place is not an option. Holes left by invalidated data can only be filled if the entire band is reclaimed, and a poor band compaction algorithm could result in spending a lot of time moving blocks over the lifetime of the device. We propose using write frequency to separate blocks to reduce data movement and develop a band compaction algorithm that implements this heuristic. We demonstrate how our algorithm results in improved data management, resulting in an up to 45% reduction in required data movements when compared to naive approaches to band management.

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Emulating a Shingled Write Disk

Cited by 18 publications

References 8 publications

Power Consumption Signature: Characterizing an SSD

Power Consumption Signature: Characterizing an SSD

IMRSim: A Disk Simulator for Interlaced Magnetic Recording Technology

Classifying Data to Reduce Long-Term Data Movement in Shingled Write Disks

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