TrackLace: Data Management for Interlaced Magnetic Recording

Wu, Fenggang; Li, Bingzhe; Zhang, Baoquan; Cao, Zhichao; Diehl, Jim; Wen, Hao; Du, David H. C.

doi:10.1109/tc.2020.2988257

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…These traces cover a wealth of business scenarios, thus are the suitable and convincing selection for testing the performance and applicability of our model. 37 Besides, the number of storage servers in the experiments is related to the traces collecting environments, that is, 13 storage servers for MSR, 6 storage servers for VDI-LUN, 1 storage servers for KC.…”

Section: Methodsmentioning

confidence: 99%

A data grouping model based on cache transaction for unstructured data storage systems

Zhu

Sun

et al. 2021

Int J of Intelligent Sys

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Cache prefetching technology has become the mainstream data access optimization strategy in the Industrial Intelligent Systems (IIS) and the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships. Therefore, researchers have to make a choice between spatial locality and temporal locality to ensure an acceptable computational complexity. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency.Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art and traditional methods, our algorithm achieves an average increase in the cache hit rate of 5%-20% on the MSR, VDI-LUN, and KC

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Section: Methodsmentioning

confidence: 99%

A data grouping model based on cache transaction for unstructured data storage systems

Zhu

Sun

et al. 2021

Int J of Intelligent Sys

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“…The IMRSim kernel module contains the following submodules: (1) the data layout and management sub-module, which simulates the layout of interlaced track and uses "zone" to manage the track space ("zone" is similar to "TG" [3,24]), ( 2) the data placement sub-module, which handles different types of requests (read, write and update) for the simulated IMR disk, (3) the address translation and I/O mapping submodule, which does the I/O redirection, (4) the availability and statistics collection sub-module, which launches a persistent thread to ensure availability, synchronize meta-data, and collect status information of the simulator. We will introduce them in detail in the following.…”

Section: Imrsim Kernel Modulementioning

confidence: 99%

“…Of course, the other multi-phase allocation strategies can be designed as needed. Also, our mapping table may be utilized to implement strategies such as track caching, hot and cold data swap, and track flipping [24,25]. Availability and Statistics Collection.…”

Section: Algorithmmentioning

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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“…These scenarios require a feature extraction method and data grouping model that can handle various types of data services. Therefore, the MSR dataset is a suitable and convincing selection for testing the performance and universal applicability of our model [34].…”

Section: Experiments and Analysismentioning

confidence: 99%

CTDGM: A Data Grouping Model Based on Cache Transaction for Unstructured Data Storage Systems

Zhu,

Du,

Sun

et al. 2020

Preprint

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Cache prefetching technology has become the mainstream data access optimization strategy in the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships, which can result in a heavy computational burden for the existing prefetching model and lead to severe degradation in the performance of data access. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art methods, our algorithm achieves an average increase in the cache hit rate of 12% on the MSR dataset with small cache size (0.001% of all the data), which in turn reduces the number of data I/O accesses by 50% when the cache size is less than 0.008% of all the data.

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TrackLace: Data Management for Interlaced Magnetic Recording

Cited by 15 publications

References 17 publications

A data grouping model based on cache transaction for unstructured data storage systems

A data grouping model based on cache transaction for unstructured data storage systems

IMRSim: A Disk Simulator for Interlaced Magnetic Recording Technology

CTDGM: A Data Grouping Model Based on Cache Transaction for Unstructured Data Storage Systems

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