Real-time failure prediction in online services

@article{Shatnawi2015RealtimeFP,
  title={Real-time failure prediction in online services},
  author={Mohammed Shatnawi and Mohamed Hefeeda},
  journal={2015 IEEE Conference on Computer Communications (INFOCOM)},
  year={2015},
  pages={1391-1399}
}
Current data mining techniques used to create failure predictors for online services require massive amounts of data to build, train, and test the predictors. These operations are tedious, time consuming, and are not done in real-time. Also, the accuracy of the resulting predictor is highly compromised by changes that affect the environment and working conditions of the predictor. We propose a new approach to creating a dynamic failure predictor for online services in real-time and keeping its… CONTINUE READING

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Key Quantitative Results

  • We show that during the production phase where the service goes through changes, our approach is able to maintain high prediction accuracy of about 86%, whereas the prediction accuracy of current stateof-the-art predictors may drop to less than 10%.

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