On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Topology discovery for large ethernet networks
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
An Efficient Approach Towards IP Network Topology Discovery for Large Multi-Subnet Networks
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
IP network topology discovery using SNMP
ICOIN'09 Proceedings of the 23rd international conference on Information Networking
SNMP-based enterprise IP network topology discovery
International Journal of Network Management
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Network topology discovery for the large IP networks is a very well studied area of research. Most of the previous work focus on improving the efficiency in terms of time and completeness of network topology discovery algorithms and less attention has been given to the deployment scenarios and user centric view of network topology discovery. In this paper we propose a novel network topology discovery algorithm and a flexible architecture. The silent features of our work are loosely coupled architecture, network boundary aware architecture, discovering the transparency of dumb/incorporative elements, flexible network Visualization, and intelligent algorithm for quick response to user discovery request. To the best of our knowledge no existing solution has focused on the above mentioned requirements. After several years of research experience in developing a complete, flexible and scalable solution for network topology discovery we propose to divide it into three loosely coupled components: topology discovery algorithm, topology object generation and persistence, and topology visualization. In this paper we will present our proposed integrated complete network topology discovery solution, discuss the motivation of our proposed architecture, the efficiency and user-friendliness of our work. Our results show that the average accuracy of our algorithm is 92.4% and takes one second to discover 100 network elements.