A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Abstract EN

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems.

Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data.

However, precise monitoring data is not easily achievable because of widespread noise.

In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise.

Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics.

A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions.

Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario.

The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream.

This study further applies ensemble classification to identify system anomaly based on the predicted metrics.

Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency.