Adaptive thresholding for proactive network problem detection

The detection of network fault scenarios has been achieved using the statistical information contained in the Management Information Base (MIB) variables. An appropriate subset of MIB variables was chosen in order to adequately describe the function of the node. The time series data obtained from these variables was analyzed using a sequential generalized likelihood ratio (GLR) test. The GLR test was used to detect the change points in the behavior of the variables. Using a binary hypothesis test, variable level alarms were generated based on the magnitude of the detected changes as compared to the normal situation. These alarms were combined using a duration filter resulting in a set of node level alarms, which correlated with the experimentally observed network faults and performance problems. The algorithm has been tested on real network data. The applicability of our algorithm to a heterogeneous node was confirmed by using the MIB data from a second node. Interestingly, for most of the faults studied, detection occurs in advance of the fault (at least 5 min) and the algorithm is simple enough for potential online implementation: thus allowing the possibility of prediction and recovery in the future.