Syslog monitoring technologies have recently received vast attentions in the areas of network management and network monitoring. They are used to address a wide range of important issues including network failure symptom detection and event correlation discovery. Syslogs are intrinsically dynamic in the sense that they form a time series and that their behavior may change over time. This paper proposes a new methodology of dynamic syslog mining in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices.The key ideas of dynamic syslog mining are 1) to represent syslog behavior using a mixture of Hidden Markov Models, 2) to adaptively learn the model using an on-line discounting learning algorithm in combination with dynamic selection of the optimal number of mixture components, and 3) to give anomaly scores using universal test statistics with a dynamically optimized threshold. Using real syslog data we demonstrate the validity of our methodology in the scenarios of failure symptom detection, emerging pattern identification, and correlation discovery.

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AbstrwtA novel approach for a stable high-order delta-sigma modulator is presented. The modulator provides 16 bit resolution at 20kHz bandwidth using 4th~order noise shaping structure at an oversampling ratio of 64. The modulation loop is completely stabilized by an FIR prediction technique. The architecture inherently has less sensitivity to com¬ ponent mismatch. The total hardware is much the same as the noise shaping integrators.

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