MACHINE LEARNING FOR PERFORMANCE IMPROVEMENT ON SSD
Currently, SSD storage systems have unstable performance caused by internal processes which can be seen in the "tail" phenomenon or elongation of the tail in CDF latency. This research uses a machine learning approach to reduce the lengthening of CDF latency. Data is replicated across a...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79447 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Currently, SSD storage systems have unstable performance caused by internal
processes which can be seen in the "tail" phenomenon or elongation of the tail in
CDF latency. This research uses a machine learning approach to reduce the
lengthening of CDF latency. Data is replicated across a number of SSDs and
machine learning determines which SSDs are considered fast SSDs from SSD user
requests. This research takes I/O data from AliBaba, Microsoft, and Tencent. The
data was run on the FEMU SSD emulator to get latency data and this data was used
to train a machine learning model using AutoML with the auto-sklearn tool. With
machine learning, it was found that performance on SSDs could be improved as
evidenced by the movement of the CDF latency graph to the left. The best models
obtained are gradient boosting models, random trees, and extra trees because these
models are ensemble models which are arrangements consisting of various different
models. |
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