Automation of Network Management with Multidisciplinary Concepts

In todays growing and ever changing world of computer networks, management systems need to have the abilities of intellectual reasoning, dynamic real time decision making, experience based self-adaptation and improvement. Furthermore, ever increasing size and complexity of computer networks require automation for their management systems. Automation minimizes human involvement which produces effective and time saving solutions for proper and dynamic supervision of these large and heterogeneous networks. In the light of above discussion, the design of an efficient, dynamic and automated network management framework requires support from the field of artificial intelligence. As the techniques based on the principles of artificial intelligence provide sophisticated abilities of intelligent decision making, experience based improvement and creative problem solving. This paper provides a survey of some important multidisciplinary research efforts that propose the use of modern techniques from the fields of Distributed Artificial Intelligence, Machine Learning and Neural Networks for improving network management.