Aberrant software-aging server detection and analysis using sliding window over LOF

@article{Li2017AberrantSS,
  title={Aberrant software-aging server detection and analysis using sliding window over LOF},
  author={Lingling Li and Huihong He and Qiang Wang and Hong xia Zhang},
  journal={2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)},
  year={2017},
  pages={652-656}
}
As software grows rapidly in size and complexity, software aging has been observed more frequently in long-running computer system failures. Ever since the notion of software aging was introduced, lots of researches have devoted in studying software aging empirically by learning aging trend under controlled experiments. However, we notice that how software aging evolves within online system is seldom covered in the literature. To fill the blank and improve usability of software-aging works, we… CONTINUE READING

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Key Quantitative Results

  • During 15 days, we find out 48 abnormal-aging servers with 93.16% degree of precision and 94.69% degree of recall by on-line detection.

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