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Area of Science:

  • Computer Science
  • Software Engineering
  • System Reliability

Background:

  • Software aging leads to performance degradation and failures in long-running systems.
  • Predicting aging-related failures (ARFs) is critical for maintaining system reliability.
  • Existing methods may not sufficiently capture the dynamic state changes indicative of aging.

Purpose of the Study:

  • To introduce a novel indicator for software aging detection.
  • To develop an accurate machine learning model for predicting aging-related failures.
  • To evaluate the proposed method on a real-world distributed database system.

Main Methods:

  • Modification of permutation entropy (PE) to Multidimensional Multi-scale Permutation Entropy (MMPE).
  • Calculation of MMPE using performance metrics collected from the Voldemort distributed database system.
  • Development of a machine learning model utilizing MMPE for anomaly detection and failure prediction.

Main Results:

  • MMPE demonstrates sensitivity to dynamic state changes in software execution.
  • The MMPE-based machine learning model achieved high accuracy in predicting software failures.
  • The proposed approach effectively detects performance anomalies indicative of software aging.

Conclusions:

  • MMPE is a promising indicator for detecting software aging and performance anomalies.
  • Machine learning models incorporating MMPE can accurately predict aging-related failures.
  • The study provides a robust method for enhancing the reliability of long-running software systems.