Using neural network in distributed management to identify control and management plane poison messages

Poison message failure propagation is a mechanism that has been responsible for large scale failures in both telecommunications and IP networks: Some or all of the network elements have a software or protocol ´bug´ that is activated on receipt of a certain network control or management message (the poison message). This activated ´bug´ would cause the node to fail with some probability. If the network control or management is such that this message is persistently passed among the network nodes, and if the node failure probability is sufficiently high, large-scale instability can result. Our previous research has been focused on centralized network management paradigm. In centralized management, one of the effective tools to deal with poison message failure is the neural network approach. However, a centralized scheme cannot be applied if the network is partitioned into several subnetworks by node failures. In this paper, we consider distributed management for the poison message problem. In particular, we use the neural network approach in a distributed way to identify the poison message.