Towards the Improvement of Diagnostic Metrics Fault Diagnosis for DSL-Based IPTV Networks Using the Rényi Entropy

Marnerides, A. K. , Malinowski, S., Morla, R., Rodrigues, M. R.D. and Kim, H. D. (2012) Towards the Improvement of Diagnostic Metrics Fault Diagnosis for DSL-Based IPTV Networks Using the Rényi Entropy. In: 2012 IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, USA, 03-07 Dec 2012, pp. 2779-2784. ISBN 9781467309219 (doi: 10.1109/GLOCOM.2012.6503537)

Abstract

IPTV networks blindly rely on the adequate operation and management of the underlying infrastructure that in numerous cases is threaten by unexpected anomalous events which consequently cause QoS degradation to the end-user. Thus, it is of great importance to deploy techniques embodied with diagnostic and self-protection metrics for determining and predicting the arrival of such events in order to proactively charge defense mechanisms without the need of an exhaustive manual inspection by the network operator. In this paper we propose and demonstrate the applicability of the Rényi entropy as a useful diagnosis feature for explicitly characterizing DSL-level anomalies issued in an IPTV network of a large European ISP. It is revealed that different orders of the Rényi entropy can formulate meaningful detection and categorization of phenomena occurring on specific Digital Subscriber Line Access Multiplexers (DSLAMs) within the DSL infrastructure. Via the synergistic exploitation of the local maxima peaks generated by each Rényi-based distribution we exhibit the feasibility to extract and identify lightweight anomalies that under simple metrics cannot be detected.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marnerides, Dr Angelos
• Authors: Marnerides, A. K., Malinowski, S., Morla, R., Rodrigues, M. R.D., and Kim, H. D.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: 2012 IEEE Global Communications Conference (GLOBECOM)
• ISSN: 1930-529X
• ISBN: 9781467309219