MACHINE LEARNING DRIFT DETECTOR FOR DRIFT MITIGATION IN I/O STREAM WORKLOAD TO CUT TAIL LATENCY
In production systems, it is common to use more than one SSD for redundant storage. These redundant SSDs can help manage incoming I/O requests to avoid overloading an SSD that is conducting its internal management tasks, such as garbage collection, wear-leveling, and write amplification, by utili...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/82433 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In production systems, it is common to use more than one SSD for redundant
storage. These redundant SSDs can help manage incoming I/O requests to avoid
overloading an SSD that is conducting its internal management tasks, such as
garbage collection, wear-leveling, and write amplification, by utilizing I/O
admission control. Machine learning algorithms are currently widely used in
various production systems that encounter continuous data, where model drift can
occur. Machine learning-based I/O admission control, as one of the applications in
production systems, also encounters model drift, which consists of concept drift and
covariate shift. Both types of drift can be mitigated by detecting their occurrence
using certain conditions, such as model accuracy and statistics-based algorithms,
and then retraining the model to adapt to the characteristics of the current data. This
study applies two schemes and introduces one innovative machine learning-based
scheme to mitigate drift. Those algorithms are, respectively, model reuse, model
reweight, and model-based drift detector. |
|---|