Flash memory based solid state drives(SSDs) have alleviated the I/O bottleneck by exploiting its data parallel design. In an enterprise environment, Flash SSD used in the form of a hybrid storage architecture to achieve the better performance with lower cost. In this architecture, I/O load balancing is one of the important factors. However, the internal parallelism distorts the performance measures of the flash SSDs. Despite the criticality of load balancing on I/O intensive environments, these studies have rarely been addressed. In this paper, we examine the effectiveness of applying classification method using machine learning techniques to the I/O saturation estimation by using Linux kernel I/O statistics instead of the utilization measure that is currently used for HDDs. We conclude that machine learning techniques that we employed (Support Vector Machine and LASSO Generalized Linear Model) performs well compared to the existing utilization measure even we cannot collect the internal information of the flash SSDs.1
|Title of host publication||CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||4|
|Publication status||Published - 2017 Nov 6|
|Event||26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore|
Duration: 2017 Nov 6 → 2017 Nov 10
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Other||26th ACM International Conference on Information and Knowledge Management, CIKM 2017|
|Period||17/11/6 → 17/11/10|
Bibliographical noteFunding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the SW Starlab support program(IITP-2017-0-00477) supervised by the IITP(Institute for Information & communications Technology Promotion).
© 2017 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)