Papers by Ronald A Kaiser
The main purpose of this work is helping operators of datacenters in the task of visualizing the... more The main purpose of this work is helping operators of datacenters in the task of visualizing the behaviour of their devices and services through time, represented by large
time series. In order to accomplish that, a technique used in pattern recognition from the financial market context was choosed. The “Perceptually Important Points” algorithm
gives a method for dimensionality reduction and a mechanism to automatically extract the most important points from a human observer perspective, favouring compression and a good visualization of time series with high dimensionality. The implementation of the algorithm and its integration in an existing monitoring system was explored and encompasses the content of this work.
Supervisory processes are fundamental when running data center operations striving for fault resi... more Supervisory processes are fundamental when running data center operations striving for fault resilience: any downtime can directly affect the business's income and definitely its reputation. Current monitoring tools rely on experts to configure constant thresholds on single streams, which is not appropriated for dynamic systems and insufficient to capture complex patterns. We present HOLMES, built to support data center experts to anticipate failures with a solution that combines Event Driven Architecture, Complex Event Processing and an unsupervised machine learning algorithm. Based on rules created by the users, the system continuously checks for known problems. Meanwhile, for the unknown ones, we leverage the CEP engine for aggregating and joining streams of real-time data to feed normalized input to FRAHST, our machine learning algorithm that detects anomalous patterns across multivariate numerical streams. We describe how the UI module also operates within the publish/subscribe paradigm to enhance situational awareness. The system had very well acceptance and was successfully implemented at one of the largest Internet Service Providers in South America.
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Papers by Ronald A Kaiser
time series. In order to accomplish that, a technique used in pattern recognition from the financial market context was choosed. The “Perceptually Important Points” algorithm
gives a method for dimensionality reduction and a mechanism to automatically extract the most important points from a human observer perspective, favouring compression and a good visualization of time series with high dimensionality. The implementation of the algorithm and its integration in an existing monitoring system was explored and encompasses the content of this work.
time series. In order to accomplish that, a technique used in pattern recognition from the financial market context was choosed. The “Perceptually Important Points” algorithm
gives a method for dimensionality reduction and a mechanism to automatically extract the most important points from a human observer perspective, favouring compression and a good visualization of time series with high dimensionality. The implementation of the algorithm and its integration in an existing monitoring system was explored and encompasses the content of this work.