Automation of Cellular Network Faults

The internet explosion and increasing number of services on offer and subscribers has placed a lot of pressure on cellular network service providers. Cellular network subscribers have different requirements and needs. This requires that the operation of the network be optimal at all times, to attract and retain subscribers. This can happen with proper operation and maintenance of the network itself. The automation of cellular network faults, where these faults are reported before they occur is the approach for avoiding the catastrophic failures that may cause network blackout. An application of Mobile Intelligent Agents (MIA) in monitoring the network elements for any potential failure of these core objects of the network to be avoided is explored in this chapter. The main concern is the prediction of possible cellular network faults using scenarios extracted from correlation of certain cellular network parameters that may not be evident to human operators. These could be solved using an advanced automated solution. This chapter proposes and discusses the development of a MIA system for computer-aided analysis, simulation and diagnosis based on mobile intelligent software agents (Wooldridge & Jennings, 1995). We propose a framework that utilizes different Artificial Intelligent (AI) techniques and probabilistic methods. Neural networks, fuzzy logic, genetic algorithms, among others, are some of the established artificial intelligent techniques used into software agents (Thottan & Ji, 1999). In this work we combine a Bayesian Network Model (BNM) with mobile intelligent agents for automating fault prediction in cellular network service providers, in a project called Modelling of Reliable Service-Based Operations Support System (MORSBOSS). The major advantage of using Bayesian network model is that the cellular network faults can be automatically detected based on a similar fault occurrence that the system has experienced previously. The information about the previous fault occurrences can be stored and retrieved from a database. This information shows the causal relation between network elements, network faults and services. It also shows the belief or likelihood of a fault at a particular network element. Fault prediction is therefore based on the historical memory of the system about known faults. This Chapter is organized as follows: In Section 2, we give a detailed overview of Cellular network faults. Definition, characteristics, causes and classification of cellular network faults are provided in this Section. Methods and algorithms of cellular network faults modeling are provided in this Section. Bayesian network, cellular network modeling process and

[1]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[2]  David Heckerman,et al.  Bayesian Networks for Knowledge Discovery , 1996, Advances in Knowledge Discovery and Data Mining.

[3]  Tony White,et al.  Mobile agents for network management , 1998, IEEE Communications Surveys & Tutorials.

[4]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[5]  F. Wotawa.  Non-monotonic Reasoning in Artificial Intelligence , 2010 .

[6]  D. Heckerman,et al.  A Bayesian Approach to Causal Discovery , 2006 .

[7]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[8]  Johnson I. Agbinya,et al.  A Probabilistic Approach To Faults Prediction in Cellular Networks , 2006, International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL'06).

[9]  Anders Holst,et al.  The Use of a Bayesian Neural Network Model for Classification Tasks , 1997 .

[10]  Jingde Cheng,et al.  Detection of Network Faults and Performance Problems , 2001 .

[11]  Marina Thottan,et al.  Proactive anomaly detection using distributed intelligent agents , 1998, IEEE Netw..

[12]  J. Frey,et al.  Multi-level Reasoning for Managing Distributed Enterprises and Their Networks , 1997, Integrated Network Management.

[13]  H. P Nii,et al.  Blackboard Systems , 1986 .

[14]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[15]  Herbert A. Simon,et al.  Applications of machine learning and rule induction , 1995, CACM.

[16]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[17]  George Eleftheriou.  Mobile Intelligent Agents for Network Management Systems , 2000 .

[18]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[19]  Heikki Mannila,et al.  Reasoning with examples: propositional formulae and database dependencies , 1999, Acta Informatica.

[20]  Carey L. Williamson,et al.  Characterization of CDMA2000 cellular data network traffic , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[21]  Chuanyi Ji,et al.  Intelligent Agents for Proactive Fault Detection , 1998, IEEE Internet Comput..

[22]  David Maxwell Chickering,et al.  Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..

[23]  Marina Thottan,et al.  Fault prediction at the network layer using intelligent agents , 1999, Integrated Network Management VI. Distributed Management for the Networked Millennium. Proceedings of the Sixth IFIP/IEEE International Symposium on Integrated Network Management. (Cat. No.99EX302).

[24]  Chuanyi Ji,et al.  Proactive network fault detection , 1997, Proceedings of INFOCOM '97.

[25]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[26]  Edgar Weippl,et al.  Using Mobile Agents in Real World: A Survey and Evaluation of Agent Platforms , 2009 .

[27]  Z. Papir.  High-performance communication networks, second edition [Book Review] , 2001, IEEE Communications Magazine.

[28]  Aaron Kershenbaum,et al.  Mobile Agents: Are They a Good Idea? , 1996, Mobile Object Systems.

[29]  Robert H. Deng,et al.  Models and algorithms for network fault detection and identification: a review , 1992, [Proceedings] Singapore ICCS/ISITA `92.

[30]  Dan Roth,et al.  Defaults and Relevance in Model-Based Reasoning , 1997, Artif. Intell..

[31]  Foster Provost,et al.  Telecommunications Network Diagnosis , 2008 .

[32]  Don Gilbert,et al.  Intelligent Agents: The Right Information at the Right Time , 1998 .

[33]  Agnar Aamodt,et al.  Case-Based Reasoning Research and Development , 1995, Lecture Notes in Computer Science.

[34]  Jean C. Walrand,et al.  High-performance communication networks , 1999 .

[35]  Yan Lin,et al.  Computational Advantages of Relevance Reasoning in Bayesian Belief Networks , 1997, UAI.

[36]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[37]  Niki Pissinou,et al.  Mobile Agents to Automate Fault Management in Wireless and Mobile Networks , 2000, IPDPS Workshops.

[38]  Dilmar Malheiros Meira.  A Model For Alarm Correlation in Telecommunications Networks , 1997 .

[39]  Johnson I. Agbinya,et al.  Prediction of Faults in Cellular Networks Using Bayesian Network Model , 2007 .

[40]  Chuanyi Ji,et al.  Automated Proactive Anomaly Detection , 1997, Integrated Network Management.