Project Overview

This project aims to explore and benchmark various machine learning models for detecting disks at high risk of experiencing fail-slow anomalies.

Notebooks and Files

experiment.ipynb
This notebook details the entire process of running the algorithms on Chameleon, including launching nodes. Currently, we only run two clusters of Perseus since Trovi has only 1GB of memory, which we cannot exceed. For access to all 25 clusters, please contact me at xikang@uchicago.edu, and I will share the repository or you can download the dataset on https://tianchi.aliyun.com/dataset/144479, then run it locally by my scripts.

results_parser.ipynb
This notebook shows the results from all the machine learning algorithms and provides analysis.

FSA-benchmark Final Report
This report offers a comprehensive introduction to all the steps involved in the project.

Directory Structure

index directory
Contains the index information for each cluster.

output
Holds the output from Chameleon.

scripts
Contains the fail-slow detection algorithms and machine learning models.

Machine Learning Models

1. Cost-Sensitive Ranking Model
   Inspired by the paper "Improving Service Availability of Cloud Systems by Predicting Disk Error" (USENIX ATC '18), this model ranks disks based on their fail-slow risk.

2. Multi-Prediction Models
   Drawing from "Improving Storage System Reliability with Proactive Error Prediction" (USENIX ATC '17), this approach uses multiple traditional machine learning models to evaluate disk health using diverse features. Various models were tested, with the Random Forest classifier proving most effective.

3. LSTM Model
   This model employs Long Short-Term Memory (LSTM) networks, trained on the first day's data for each cluster and evaluated on data spanning all days. It captures temporal dependencies to accurately predict fail-slow anomalies over time.

4. PatchTST Model
   An advanced sequence model that leverages transformers to handle time series prediction and fail-slow detection.

5. GPT-4o-mini
   A large language model used to analyze disk metrics and detect fail-slow conditions. Please replace openai_api_key in the code where necessary.

For detailed implementation of these models, please refer to the "FSA-benchmark Final Report."