DIG: Rapid Characterization of Modern Hard Disk Drive and Its Performance Implication

Gim, Jongmin; Won, Youjip; Chang, Jong San; Shim, Jaehoon; Park, Y.

doi:10.1109/snapi.2008.13

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…are similar (Gim et al 2008) (as shown in Figs. 4(a), (c) and (e) for three different hard-disks) and only their timing parameters (p, q, r and s in (3)) are different.…”

Section: Disk Modeling By Annsupporting

confidence: 73%

“…Extracting these parameters has been the subject of intense researches for more than a decade (Ruemmler and Wilkes 1994;Gim et al 2008;Worthington et al 1995;Aboutabl et al 1997;Schlosser et al 2005). Despite its importance in performance evaluation, it is hardly possible to build practically meaningful model due to complexity.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Different algorithms have been used for extracting seek curves depending on whether the end goal is accurate disk modeling or high-performance disk request scheduling (Ruemmler and Wilkes 1994; Gim et al 2008;Worthington et al 1995;Aboutabl et al 1997;Schlosser et al 2005;Chiueh and Huang 2002;Schindler et al 2002). Because, it is hardly possible to build practically meaningful formula due to complexity of seek curve, a neural network based model has been proposed here (see Sect.…”

Section: Problem Descriptionmentioning

confidence: 99%

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Hardware design of a new genetic based disk scheduling method

et al. 2010

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Disk management is an increasingly important aspect of operating systems research and development because it has great effect on system performance. As the gap between processor and disk performance continues to increase in modern systems, access to mass storage is a common bottleneck that ultimately limits overall system performance. In this paper, we propose hardware architecture of a new genetic based real-time disk scheduling method. Also, to have a precise simulation, a neural network is proposed to simulate seek-time of disks. Simulation results showed the hardware implementation of proposed algorithm outperformed software implementation in term of execution time, and other related works in terms of number of tasks that miss deadlines and average seeks.

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“…are similar (Gim et al 2008) (as shown in Figs. 4(a), (c) and (e) for three different hard-disks) and only their timing parameters (p, q, r and s in (3)) are different.…”

Section: Disk Modeling By Annsupporting

confidence: 73%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

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Hardware design of a new genetic based disk scheduling method

et al. 2010

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“…Extracting these parameters has been the subject of intense researches for more than a decade [36][37][38][39][40]. For example in a HP 97560 hard disk, the seek-time with movement distance D j,i =(9a i Àa j 9) is calculated as follows [36]: yT w(i) yT w(n) ; schedule fulfill time (f w(n) ) is the finish time of the latest task (T w(n) ) and b w(i) the data size of request T w(i) .…”

Section: Problem Descriptionmentioning

confidence: 99%

A genetic based disk scheduling method to decrease makespan and missed tasks

Bonyadi

Rahmani

Moghaddam

2010

Information Systems

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“…Shim and Park [10] developed Disk Geometry Analyzer (DIG) which efficiently extracted comprehensive internal information and characterized the performance metric of hard disk drives.…”

Section: Previous Workmentioning

confidence: 99%

Benchmarked Hard Disk Drive Performance Characterization and Optimization Based on Design of Experiments Techniques

Lin¹

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Benchmarked hard disk drive performance characterization and optimization based on Design of Experiments techniques Yu-wei LinThis paper describes an experimental study offered by Designs of Experiments (DOE) within the defined factor domains to evaluate the factor effects of simultaneous characteristics on the benchmarked hard disk drive performance by proposing well-organized statistical models for optimizations. The numerical relations of the obtained models permit to predict the behaviors of benchmarked disk performances as functions of significant factors to optimize relevant criteria based on the needs.The experimental data sets were validated to be in satisfying agreement with predicted values by analyzing the response surface plots, contour plots, model equations, and optimization plots. The adequacy of the model equations were verified effectively by a prior generation disk drive within the same model family.The retained solutions for potential industrializations were the concluded response surface models of benchmarked disk performance optimizations.The comprehensive benchmarked performance modeling procedure for hard disk drives not only saves experimental costs on physical modeling but also leads to hard-to-find quality improvement solutions to manufacturing decisions.iv Acknowledgements First and foremost I would like to thank my thesis advisor, Dr. John Y. Oliver for allowing me to join this project, for his support, for his advice, and for his kindness.I would also like to acknowledge Dr. Jianbiao (John) Pan for his guidance, for his expertise, and especially for his patience during the development in this thesis.My appreciation also goes to Dr. James G. Harris for his feedback and for his participation on my thesis committee.

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DIG: Rapid Characterization of Modern Hard Disk Drive and Its Performance Implication

Cited by 11 publications

References 3 publications

Hardware design of a new genetic based disk scheduling method

Hardware design of a new genetic based disk scheduling method

A genetic based disk scheduling method to decrease makespan and missed tasks

Benchmarked Hard Disk Drive Performance Characterization and Optimization Based on Design of Experiments Techniques

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