Efficient failure prediction in autonomic networks based on trend and frequency analysis of anomalous patterns

Hesham J. Abed, Ala Al-Fuqaha, Bilal Khan, Ammar Rayes

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We describe an efficient failure prediction system based on new algorithms that model and detect anomalous behaviors using multi-scale trend analysis of multiple network parameters. Our approach enjoys many advantages over prior approaches. By operating at multiple timescales simultaneously, the new system achieves robustness against unreliable, redundant, incomplete and contradictory information. The algorithms employed operate with low time complexity, making the system scalable and feasible in real-time environments. Anomalous behaviors identified by the system can be stored efficiently with low space complexity, making it possible to operate with minimal resource requirements even when processing high-rate streams of network parameter values. The developed algorithms generate accurate failure predictions quickly, and the system can be deployed in sa distributed setting. Prediction quality can be improved by considering larger sets of network parameters, allowing the approach to scale as network complexity increases. The system is validated by experiments that demonstrate its ability to produce accurate failure predictions in an efficient and scalable manner.

Original languageEnglish (US)
Pages (from-to)186-213
Number of pages28
JournalInternational Journal of Network Management
Volume23
Issue number3
DOIs
StatePublished - May 1 2013

Fingerprint

Processing
Experiments

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Efficient failure prediction in autonomic networks based on trend and frequency analysis of anomalous patterns. / Abed, Hesham J.; Al-Fuqaha, Ala; Khan, Bilal; Rayes, Ammar.

In: International Journal of Network Management, Vol. 23, No. 3, 01.05.2013, p. 186-213.

Research output: Contribution to journalArticle

@article{961b006ad1144d60a1a382b72b12dfab,
title = "Efficient failure prediction in autonomic networks based on trend and frequency analysis of anomalous patterns",
abstract = "We describe an efficient failure prediction system based on new algorithms that model and detect anomalous behaviors using multi-scale trend analysis of multiple network parameters. Our approach enjoys many advantages over prior approaches. By operating at multiple timescales simultaneously, the new system achieves robustness against unreliable, redundant, incomplete and contradictory information. The algorithms employed operate with low time complexity, making the system scalable and feasible in real-time environments. Anomalous behaviors identified by the system can be stored efficiently with low space complexity, making it possible to operate with minimal resource requirements even when processing high-rate streams of network parameter values. The developed algorithms generate accurate failure predictions quickly, and the system can be deployed in sa distributed setting. Prediction quality can be improved by considering larger sets of network parameters, allowing the approach to scale as network complexity increases. The system is validated by experiments that demonstrate its ability to produce accurate failure predictions in an efficient and scalable manner.",
author = "Abed, {Hesham J.} and Ala Al-Fuqaha and Bilal Khan and Ammar Rayes",
year = "2013",
month = "5",
day = "1",
doi = "10.1002/nem.1825",
language = "English (US)",
volume = "23",
pages = "186--213",
journal = "International Journal of Network Management",
issn = "1055-7148",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

TY - JOUR

T1 - Efficient failure prediction in autonomic networks based on trend and frequency analysis of anomalous patterns

AU - Abed, Hesham J.

AU - Al-Fuqaha, Ala

AU - Khan, Bilal

AU - Rayes, Ammar

PY - 2013/5/1

Y1 - 2013/5/1

N2 - We describe an efficient failure prediction system based on new algorithms that model and detect anomalous behaviors using multi-scale trend analysis of multiple network parameters. Our approach enjoys many advantages over prior approaches. By operating at multiple timescales simultaneously, the new system achieves robustness against unreliable, redundant, incomplete and contradictory information. The algorithms employed operate with low time complexity, making the system scalable and feasible in real-time environments. Anomalous behaviors identified by the system can be stored efficiently with low space complexity, making it possible to operate with minimal resource requirements even when processing high-rate streams of network parameter values. The developed algorithms generate accurate failure predictions quickly, and the system can be deployed in sa distributed setting. Prediction quality can be improved by considering larger sets of network parameters, allowing the approach to scale as network complexity increases. The system is validated by experiments that demonstrate its ability to produce accurate failure predictions in an efficient and scalable manner.

AB - We describe an efficient failure prediction system based on new algorithms that model and detect anomalous behaviors using multi-scale trend analysis of multiple network parameters. Our approach enjoys many advantages over prior approaches. By operating at multiple timescales simultaneously, the new system achieves robustness against unreliable, redundant, incomplete and contradictory information. The algorithms employed operate with low time complexity, making the system scalable and feasible in real-time environments. Anomalous behaviors identified by the system can be stored efficiently with low space complexity, making it possible to operate with minimal resource requirements even when processing high-rate streams of network parameter values. The developed algorithms generate accurate failure predictions quickly, and the system can be deployed in sa distributed setting. Prediction quality can be improved by considering larger sets of network parameters, allowing the approach to scale as network complexity increases. The system is validated by experiments that demonstrate its ability to produce accurate failure predictions in an efficient and scalable manner.

UR - http://www.scopus.com/inward/record.url?scp=84878395969&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878395969&partnerID=8YFLogxK

U2 - 10.1002/nem.1825

DO - 10.1002/nem.1825

M3 - Article

AN - SCOPUS:84878395969

VL - 23

SP - 186

EP - 213

JO - International Journal of Network Management

JF - International Journal of Network Management

SN - 1055-7148

IS - 3

ER -