Institutional Repository
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Topology discovery in heterogeneous IP networks: the NetInventory system

Breitbart, Y, Garofalakis Minos, Ben Jai, Martin Cliff, Rastogi Rajeev, Silberschatz Avi

Full record


URI: http://purl.tuc.gr/dl/dias/D4BE1AC8-8219-4BD7-8A65-BB48084426CC
Year 2004
Type of Item Peer-Reviewed Journal Publication
License
Details
Bibliographic Citation Y. Breitbart, M. Garofalakis, B. Jai, C. Martin, R. Rastogi and A. Silberschatz, "Topology discovery in heterogeneous IP networks: the NetInventory system", IEEE/ACM Trans. Network., vol. 12, no. 3, pp. 401-414, Jun. 2004. doi:10.1109/TNET.2004.828963 https://doi.org/10.1109/TNET.2004.828963
Appears in Collections

Summary

Knowledge of the up-to-date physical topology of an IP network is crucial to a number of critical network management tasks, including reactive and proactive resource management, event correlation, and root-cause analysis. Given the dynamic nature of today's IP networks, keeping track of topology information manually is a daunting (if not impossible) task. Thus, effective algorithms for automatically discovering physical network topology are necessary. Earlier work has typically concentrated on either 1) discovering logical (i.e., layer-3) topology, which implies that the connectivity of all layer-2 elements (e.g., switches and bridges) is ignored, or 2) proprietary solutions targeting specific product families. In this paper, we present novel algorithms for discovering physical topology in heterogeneous (i.e., multi-vendor) IP networks. Our algorithms rely on standard SNMP MIB information that is widely supported by modern IP network elements and require no modifications to the operating system software running on elements or hosts. We have implemented the algorithms presented in this paper in the context of the NetInventory topology-discovery tool that has been tested on Lucent's own research network. The experimental results clearly validate our approach, demonstrating that our tool can consistently discover the accurate physical network topology with reasonably small running-time requirements even for fairly large network configurations.

Services

Statistics