Neural network for saturation prediction of solid state drives

Jaehyung Kim, Jinuk Park, Sanghyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

State-of-the-art storage devices that have parallel capability have significantly reduced the performance gap between processor and storage I/O. However, the internal parallelism makes it difficult to measure utilization that can be used as a basis of load balancing, which is a critical feature of performance improvement of parallel systems. When utilization of storage reaches to one hundred percent, the I/O saturation occurs, and then some of I/O loads need to be redirected to the other storage to improve the whole storage performance. There is, to my best knowledge, no studies in I/O saturation prediction of the flash SSDs regarding the internal parallelism that the previous HDD based measure cannot reflect. In this paper, we propose I/O saturation prediction method based on ANN (Artificial Neural Network) using kernel I/O statistics and various performance measures. We extracted I/O statistics and performance measures by conducting I/O workload simulation especially on NVMe (Non-Volatile Memory Express) flash SSD so that we can get as many observations from various I/O characteristics as flash SSD possibly can show. We constructed ANN model and compared with SVM (Support Vector Machine) model. The evaluation shows that ANN performs well compared to the existing utilization measure which assumes that HDD can only perform single instruction at a time.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2069-2074
Number of pages6
ISBN (Electronic)9781538616451
DOIs
Publication statusPublished - 2017 Nov 27
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: 2017 Oct 52017 Oct 8

Publication series

Name2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
CountryCanada
CityBanff
Period17/10/517/10/8

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

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  • Cite this

    Kim, J., Park, J., & Park, S. (2017). Neural network for saturation prediction of solid state drives. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (pp. 2069-2074). (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2017.8122924