AIHS

Overview

AIHS is an machine-learning-based self-healing prototype that provides automated intelligent healing for cloud-scale data centers. This source code showcases the complete workflow of AIHS and its usages.

Prerequisite

AIHS is built in Python and requires the following dependencies:

• Python3

• Numpy, pandas

  •   pip3 install numpy
      pip3 install pandas

• Sklearn, lightgbm

  •   pip3 install sklearn
      pip3 install lightgbm

Source code

The source code include the following file and directories:

• DataGenerator.py
  • The script that generates the synthetic dataset for verifying the AIHS workflow
• AIHS_Training.py
  • The script that AIHS trains the machine learning models for the Mapper, Predictor, and Controller
• AIHS_api.py
  • The script that implement the three-stage workflow of AIHS
  • Runing this script to find repair action for each given monitoring logs
• dataset
  • The directory where a small synthetic dataset is provided
• model
  • The directory where the well-trained models is provided

Examples

Following we show how to run the example we provided:

•   ### Step 1: generate the dataset
    python3 DataGenerator.py 
    # the synthetic records will be generated and overwrite the ./dataset/train.csv
    # modified the source code can change the number of records to generate
    # by defualt, the provided trace incldue 500 records
    # df=DG.gen_data(500,'./Dataset/train.csv')

•   ### Step 2: train the model
    python3 AIHS_Training.py 
    # Trained the models for Mapper, Predictor, and Controller uisng the trace in ./Dataset
    # 
    # change the parameters for following functions to choose different models.
    # mapper=Mapper('LDA',df)
    # predictor=Predictor(df,mapper,'gbdt')
    # controller=Controller(mapper,df,'gbdt')
    # 
    # by defualt, we use LDA in the Mapper, GDBT in Predictor and Controller
    # Three well-trained model is put in ./model

•   ### Step 3: simulate the self-healing workflow
    python3 AIHS_api.py 
    # Run the self-healing for the monitoring log example we given in the script
    # User can chagne to any other monitoring log to check the output 

• Sample output

  • {'hostname': '12x8dj02', 'timestamp': 12345, 'action': ['RI']}