SMR drive performance analysis under different workload environments

As the main stream of Hard Disk Drive (HDD) techniques, Shingled Magnetic Recording (SMR) drives have unique features different from conventional disk drives, e.g., append-only (sequential) write, indirect address mapping and garbage collection. Batch process is also designed to fully utilize the se...

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Main Authors: Niu, Junpeng, Xie, Mingzhou, Xu, Jun, Xie, Lihua, Xia, Li
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140323
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1403232020-05-28T03:09:21Z SMR drive performance analysis under different workload environments Niu, Junpeng Xie, Mingzhou Xu, Jun Xie, Lihua Xia, Li School of Electrical and Electronic Engineering Delta-NTU Corporate Laboratory for Cyber–Physical Systems Engineering::Electrical and electronic engineering SMR Drive Queuing Theory As the main stream of Hard Disk Drive (HDD) techniques, Shingled Magnetic Recording (SMR) drives have unique features different from conventional disk drives, e.g., append-only (sequential) write, indirect address mapping and garbage collection. Batch process is also designed to fully utilize the sequential write property to improve the SMR drive performance. The selection of different system parameters and policies affects the system performance and capacity efficiency. However, there is no dedicated analytical tool available so far to guide the parameter and policy selection. A queuing model is built and solved through a Markov chain process for the SMR drive to analyze the system performance under different kinds of system settings and workload environments. the control policies and parameter settings are also studied to explore their relation to the system performance of SMR disks, from the point of views of both analytical model and numerical simulation. An adaptive Garbage Collection (GC) policy is proposed to automatically select the foreground GC and background GC to provide consistent drive performance. A SMR drive simulator is further developed to validate the model and check the performance impacts of different drive parameters. We illustrate the similarity of the analytical and simulation results, and show that our tool can be utilized as the guide of the SMR drive design. 2020-05-28T03:09:21Z 2020-05-28T03:09:21Z 2018 Journal Article Niu, J., Xie, M., Xu, J., Xie, L., & Xia, L. (2018). SMR drive performance analysis under different workload environments. Control Engineering Practice, 75, 86-97. doi:10.1016/j.conengprac.2018.03.016 0967-0661 https://hdl.handle.net/10356/140323 10.1016/j.conengprac.2018.03.016 2-s2.0-85044978668 75 86 97 en Control Engineering Practice © 2018 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
SMR Drive
Queuing Theory
spellingShingle Engineering::Electrical and electronic engineering
SMR Drive
Queuing Theory
Niu, Junpeng
Xie, Mingzhou
Xu, Jun
Xie, Lihua
Xia, Li
SMR drive performance analysis under different workload environments
description As the main stream of Hard Disk Drive (HDD) techniques, Shingled Magnetic Recording (SMR) drives have unique features different from conventional disk drives, e.g., append-only (sequential) write, indirect address mapping and garbage collection. Batch process is also designed to fully utilize the sequential write property to improve the SMR drive performance. The selection of different system parameters and policies affects the system performance and capacity efficiency. However, there is no dedicated analytical tool available so far to guide the parameter and policy selection. A queuing model is built and solved through a Markov chain process for the SMR drive to analyze the system performance under different kinds of system settings and workload environments. the control policies and parameter settings are also studied to explore their relation to the system performance of SMR disks, from the point of views of both analytical model and numerical simulation. An adaptive Garbage Collection (GC) policy is proposed to automatically select the foreground GC and background GC to provide consistent drive performance. A SMR drive simulator is further developed to validate the model and check the performance impacts of different drive parameters. We illustrate the similarity of the analytical and simulation results, and show that our tool can be utilized as the guide of the SMR drive design.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Niu, Junpeng
Xie, Mingzhou
Xu, Jun
Xie, Lihua
Xia, Li
format Article
author Niu, Junpeng
Xie, Mingzhou
Xu, Jun
Xie, Lihua
Xia, Li
author_sort Niu, Junpeng
title SMR drive performance analysis under different workload environments
title_short SMR drive performance analysis under different workload environments
title_full SMR drive performance analysis under different workload environments
title_fullStr SMR drive performance analysis under different workload environments
title_full_unstemmed SMR drive performance analysis under different workload environments
title_sort smr drive performance analysis under different workload environments
publishDate 2020
url https://hdl.handle.net/10356/140323
_version_ 1681058791075872768