Automatic network restoration using a two-level associative memory

Author

Wang, Chia-Jiu ; Zhou, Hong Ying ; Chow, Ching-Hua

Author_Institution

Dept. of Electr. Eng., Colorado Univ., Colorado Springs, CO, USA

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3565

Abstract

Based on the principles of neural computing, a new approach in network restoration is proposed and implemented in this paper. This new approach, called NRNN (network restoration using neural networks), is a hybrid algorithm and is implemented by using a two-level associative memory. Compared to NETSPAR (a hybrid algorithm proposed by Bellcore in 1991), NRNN not only obtains 100% recovery of disrupted traffic due to a link or node failure but also requires a very small memory at each node. At a node, the memory requirement for NRNN is less than 1% of the memory requirement for NETSPAR

Keywords

associative processing; content-addressable storage; fault tolerant computing; neural nets; automatic network restoration; neural networks; node failure; two-level associative memory; Associative memory; Computer networks; Computer science; Distributed algorithms; Network-on-a-chip; Neural networks; Optical fibers; Routing; Springs; Telecommunication traffic;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374909

Filename

374909