Asian Journal of Engineering and Technology Innovation

Asset Data Linkage USING Artificial Intelligence Method of Machine Learning

Krishna M V

Reva Institute of Technology and Management, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Near Border Security Bustop, Bengaluru, Karnataka-560064, India.

Abstract

As organizations grow so do their IT system, while this is positive phenomenon it brings with itself numerous challenges. One of the challenges that are resultant of such growth is the management of vast amount of asset data (metadata) in a meaningful and relevant fashion. In situations where there is lack of adequate measures to manage asset data of key organizational systems it paves way to a potential issue that can impede management of IT operations and hence quality IT service. Effective and efficient management of asset data is an often ignored but an important part of the IT operations management. The problems of management of IT asset data described here is about identifying the hosts and linking them together based on their architectural relationship, this information is useful for a variety of operational uses such as asset change impact assessment, decommissioning of servers, etc. Unsupervised Machine learning (ML) methods of clustering can be applied to the IP traffic observed across the network to group hosts into sets that have similar communicating partners. Self-Organizing Map (SOM) an Artificial Neural Network trains itself on IP logs entries and forms clusters of those communication patterns.

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