Decentralized boolean network tomography based on network partitioning

Abstract

Network tomography is a promising technique to achieve fault management in networks where the existing IP-based troubleshooting mechanism cannot be used. Aiming to apply Boolean network tomography to fault management, various heuristic methods for configuring monitoring trails have been proposed to localize link failures in all-optical mesh networks. However, these existing heuristic methods must be executed in a centralized server that administers the entire managed network, and present scalability problems when they are applied to large-scale managed networks. Thus, this paper proposes a novel scheme for achieving lightweight Boolean network tomography in a decentralized manner. The proposed scheme partitions the managed network into multiple management areas and localizes link failures independently within each area. This paper also proposes a heuristic network partition method with the aim of implementing the proposed scheme efficiently. The effectiveness of the proposed scheme is verified using a typical fault management scenario, where all the single-link failures are localized by the monitoring paths the routes of which are predetermined. Simulation results show that the proposed scheme can greatly reduce the computational load on the fault management server when Boolean network tomography is deployed in large-scale managed networks.