Spin Retry Count Relation with Other Hdd Parameters

Authors

  • Iskandar Nailovich Nasyrov Kazan Federal University. Author
  • Ildar Iskandarovich Nasyrov Kazan Federal University. Author
  • Rustam Iskandarovich Nasyrov Kazan Federal University. Author
  • Bulat Askarovich Khairullin Kazan Federal University. Author

DOI:

https://doi.org/10.61841/fycytr46

Keywords:

Rotation Mechanism, Hard Drive, Reliability, Information, Security, Drive.

Abstract

The change of the SMART parameter 10 Spin retry count values depending on the operating time is considered; this parameter characterizes the number of repeated attempts to spin the disks up to operating speed if the first attempt was unsuccessful. This parameter is critical in the sense that if the value of the attribute increases, then the likelihood of malfunctions in the mechanical part of the hard disk drives is high. The scientific task of the study is to establish the relationship between this parameter in failed hard drives and the values of other reliability parameters for information stores from various manufacturers.In the course of the study, the drives of the HGST, Hitachi, Samsung, ST, Toshiba, and WDC trademarks operated in the Backblaze largest commercial data centre were analysed. As a result of the analysis, the relationship between the specified parameter and such parameters as 3 spinup time (time of spinning the disk package from standstill to operating speed), 4 start/stop count (counting the spindle start/stop cycles), 12 power cycle count (number of fulldrive switching on/off cycles), 192 power-off retract count (the number of shutdown cycles, including emergencies), and 193 load cycle count (the number of magnetic head block moves in the parking zone/in working position cycles). It is shown that the nature of the change in the values of the considered parameters depends on the manufacturer of the hard drives. It is proposed to carry out an individual assessment of the information storage device rotation mechanism reliability using the parameters identified as a result of the study. 

Downloads

Download data is not yet available.

References

[1] S.M.A.R.T. From Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/S.M.A.R.T. Checked 24.03.2019.

[2] Hard Drive Data and Stats/Backblaze. URL: https://www.backblaze.com/b2/hard-drive-test-data.html. Checked 24.03.2019.

[3] Nasyrov I.N., Nasyrov I.I., Nasyrov R.I., Khairullin B.A. Data mining for information storage reliability assessment by relative values

International Journal of Engineering and Technology (UAE). 2018. Vol.7, Is.4.7 Special Issue 7. P.204-208. URL:

https://www.sciencepubco.com/index.php/ijet/article/view/20545.

[4] Nasyrov I.N., Nasyrov I.I., Nasyrov R.I., Khairullin B.A. Parameters selection for information storage reliability assessment and prediction

by absolute values // Journal of Advanced Research in Dynamical and Control Systems. 2018. Vol.10, Is.2 Special Issue. P.2248-2254.

URL: http://jardcs.org/backissues/abstract.php?archiveid=5363.

[5] Nasyrov I.N., Nasyrov I.I., Nasyrov R.I., Khairullin B.A. Dependence of reallocated sectors count on HDD power-on time // International

Journal of Engineering and Technology (UAE). 2018. Vol.7, Is.4.7 Special Issue 7. P.200-203. URL:

https://www.sciencepubco.com/index.php/ijet/article/view/20544.

[6] Rincón C.A.C., Paris J.-F., Vilalta R., Cheng A.M.K., Long D.D.E. Disk failure prediction in heterogeneous environments // Proceedings

of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2017. Seattle, WA,

USA, July 9-12, 2017. URL: http://ieeexplore.ieee.org/document/8046776/.

[7] Qian J., Skelton S., Moore J., Jiang H. P3: Priority-based proactive prediction for soon-to-fail disks // Proceedings of the 10th IEEE

International Conference on Networking, Architecture, and Storage, NAS 2015. Boston, MA, USA, August 6-7, 2015. – 7255224. – p. 81

86. URL: http://ieeexplore.ieee.org/document/7255224/.

[8] Botezatu M.M., Giurgiu I., Bogojeska J., Wiesmann D. Predicting disk replacement towards reliable data centers/Proceedings of the 22nd

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16. San Francisco, California, USA, August

13-17, 2016. – p. 39-48. URL: https://dl.acm.org/citation.cfm?doid=2939672.2939699.

[9] Chaves I.C., de Paula M.R.P., Leite L.G.M., Queiroz L., Pordeus J.P., Machado J.C. BaNHFaP: A Bayesian Network-Based Failure

Prediction Approach for Hard Disk Drives // Proceedings of the 5th Brazilian Conference on Intelligent Systems, BRACIS 2016. Recife,

Pernambuco, BR, October 9-12, 2016. – 7839624. – p. 427-432. URL: http://ieeexplore.ieee.org/document/7839624/.

[10] Gaber S., Ben-Harush O., Savir A. Predicting HDD failures from compound SMART attributes // Proceedings of the 10th ACM

International Systems and Storage Conference, SYSTOR '17. Haifa, Israel, May 22-24, 2017. Article No. 31. URL:

https://dl.acm.org/citation.cfm?doid=3078468.3081875.

[11] Gopalakrishnan P.K., Behdad S. Usage of product lifecycle data to detect hard disk drive failure factors // Proceedings of the ASME

International Design Engineering Technical Conference. Cleveland, Ohio, USA, August 6–9, 2017. URL:

http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2662132.

Downloads

Published

30.08.2019

How to Cite

Spin Retry Count Relation with Other Hdd Parameters. (2019). International Journal of Psychosocial Rehabilitation, 23(3), 765-775. https://doi.org/10.61841/fycytr46