Abstract: A technique for detecting changes in a signal that is measured and reported by quantization uses a model that is updated in response to the sampling of quantized values representing the signal. In one stage, (i) frequencies of occurrences of different sampled quantized values in the stage are calculated and (ii) mean frequencies for each of the different sampled quantized values in the stage are calculated and recorded. In a next stage, frequencies of occurrences of different sampled quantized values occurring after an end of the preceding stage are calculated and statistically compared with the mean frequencies of the different sampled quantized values determined in the preceding stage. Dependent on this comparison, a notification may be issued indicating the signal is anomalously changing.

Abstract: One embodiment of the present invention provides a system that systematically monitors and records performance information in a computer system. During operation, the system periodically measures a number of performance parameters in the computer system. The system then stores data representing values for the measured performance parameters in a long-term storage space. Next, the system recurrently compresses data stored in the long-term storage space, thereby allowing additional data representing newly collected performance information to be stored in the long-term storage space.

Abstract: In one embodiment, a method and apparatus for high-efficiency time-series archiving for computer server telemetry signals is disclosed. The method includes selecting one or more telemetry signals of a plurality of telemetry signals by a sequential probability ratio test (SPRT) algorithm, the SPRT algorithm identifying the one or more telemetry signals as not consistent with normal behavior of the plurality of telemetry signals, injecting synthetic samples around the selected one or more telemetry signals to create a continuous time series telemetry sample, and analyzing the continuous time series telemetry sample to identify leading indicators of faults in the target. Other embodiments are also disclosed.

Abstract: One embodiment of the present invention provides a system that determines a severity of degradation in a signal. During operation, the system receives signal values for the signal, wherein the signal values are received with a constant sampling interval. Next, for each received signal value, the system applies a Sequential Probability Ratio Test (SPRT) to the signal value. If the SPRT generates an alarm on the signal value, the system increments a cumulative counter which records a running total number of the SPRT alarms. Upon receiving each signal value, the system updates a cumulative function using a value in the cumulative counter. Next, the system determines the severity of degradation in the signal from the shape of the cumulative function.

Abstract: One embodiment of the present invention provides a system that optimizes a regression model which predicts a signal as a function of a set of available signals. During operation, the system receives training data for the set of available signals from a computer system during normal fault-free operation. The system also receives an objective function which can be used to evaluate how well a regression model predicts the signal. Next, the system initializes a pool of candidate regression models which includes at least two candidate regression models, wherein each candidate regression model in the pool includes a subset of the set of available signals.

Abstract: One embodiment of the present invention provides a system that determines a severity of degradation in a signal. During operation, the system receives signal values for the signal, wherein the signal values are received with a constant sampling interval. Next, for each received signal value, the system applies a Sequential Probability Ratio Test (SPRT) to the signal value. If the SPRT generates an alarm on the signal value, the system increments a cumulative counter which records a running total number of the SPRT alarms. Upon receiving each signal value, the system updates a cumulative function using a value in the cumulative counter. Next, the system determines the severity of degradation in the signal from the shape of the cumulative function.

Abstract: One embodiment of the present invention provides a system that facilitates high-sensitivity detection of an anomaly in a signal, wherein the signal is sampled to produce a set of possible quantized signal values. During operation, the system constructs a “reference distribution” for an “occurrence frequency” of a specific quantized signal value from the set of possible quantized signal values. The system then obtains a “deviant distribution” associated with the reference distribution, wherein the deviant distribution has an offset from the reference distribution to indicate an anomaly in the signal. Next, in response to a new occurrence of the specific quantized signal value, the system updates a mean and a variance of the reference distribution for the specific quantized signal value. The system also adjusts the deviant distribution for the specific quantized signal value based on the updated mean and the updated variance of the reference distribution for the specific quantized signal value.

Abstract: A system that generates a dynamic trace of power consumption in a computer system. The system periodically polls current sensors and associated voltage sensors within the computer system to generate dynamic traces of currents and associated voltages for individual components within the computer system. The system then generates a dynamic trace of total power consumption for the computer system based on the dynamic traces of the currents and the associated voltages for the constituent components.

Abstract: A system that uses statistical techniques to selectively transmit data from a sensor. During operation, the system receives a sequence of quantized values from the sensor. The system then determines whether a distribution for the sequence of quantized values indicates that the sensor is observing a real event. If so, the system transmits sensor data for the real event to a receiver.

Abstract: A system that uses statistical techniques to selectively transmit sensor data which is likely to be of interest. During operation, the system uses statistical techniques to compute a likelihood that the sensor is observing a real event. Next, the system compares the likelihood to an upper threshold. If the likelihood is greater than or equal to the upper threshold, the system determines that the sensor is observing a real event and transmits sensor data for the real event to a receiver.