Efficient Signal Selection for Nonlinear System-Based Models of Enterprise Servers

Modern computer systems are equipped with a significant number of hardware and software sensors from which time series telemetry data can be captured for analysis. One particularly interesting application of the time series data is proactive fault monitoring- the ability to identify leading indicators of failure before the failure actually occurs. Advanced pattern recognition approaches based on nonlinear system-based models are frequently used in proactive fault monitoring, whereby the complex interactions among multivariate signal behaviors are captured. For such approaches, a model is constructed in the training phase, during which the (nonlinear) correlations among the multiple input signals are learned. In the subsequent surveillance phase, the value of each signal is estimated as a function of the other signals. Significant deviations between the estimates and observed signals indicate a potential anomaly in the system under surveillance. Choosing an appropriate subset of signals to monitor largely has been an exercise in engineering judgment, rudimentary linear correlation analysis, and trial-and-error. This paper presents a genetic algorithm approach at signal selection that efficiently identifies a near-optimal model based upon multiple criteria