Alarm correlation for congestion diagnosis in ATM networks

In this work, we examine the tradeoff between fine-grade and coarse-grade OAM measurements. The fine-grade OAM measurement, with the overhead of voluminous measurement information, leads to pin-pointed identification when problems occur; while coarse-grade OAM measurement, without detailed information, needs to correlate many alarms which may be triggered by a single problem. Alarm correlation, however, requires some heuristics for problem diagnosis and has some degree of uncertainty. Taking VP(virtual path) congestion diagnosis as our target example, we present the path intersection heuristics as the alarm correlation method to analyze the congestion alarms and find the congestion areas. Our simulation results show that the intersection heuristics with output link consideration locates the congestion nodes precisely. We also analyze the congestion pattern and find that the congestion area has the tendency of expanding from one node to its neighbors. After identifying the congested nodes, we apply three methods to choose the congested VPCs for rerouting. The results show that the method of Less Summed Capacity performs the best in terms of average rerouted capacity. In the future, we plan to implement the heuristics into the network management system of a practical ATM testbed. This includes two parts: the agents which perform coarse-grade OAM measurement and the manager which collects the alarms, correlates them to locate the source of congestion, and furthermore, applies the VP rerouting.