On Predictability of System Anomalies in Real World

Abstract

As computer systems become increasingly complex, system anomalies have become major concerns in system management. In this paper, we present a comprehensive measurement study to quantify the predictability of different system anomalies. Online anomaly prediction allows the system to foresee impending anomalies so as to take proper actions to mitigate anomaly impact. Our anomaly prediction approach combines feature value prediction with statistical classification methods. We conduct extensive measurement study to investigate anomalous behavior of three systems in the real world: PlanetLab, SMART hard drive data, and IBM System S. We observe that real world system anomalies do exhibit predictability, which can be predicted with high accuracy and significant lead time.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2011
Accession Number
ADA558228

Entities

People

  • Xiaohui Gu
  • Yongmin Tan

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Anomaly Detection
  • Bayesian Networks
  • Change Detection
  • Data Sets
  • Detection
  • Detectors
  • Lead Time
  • Machine Learning
  • Markov Chains
  • Measurement
  • Models
  • Probability
  • Probability Distributions
  • Standards
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Theoretical Analysis.