SEASONAL TRENDING, FORECASTING, ANOMALY DETECTION, AND ENDPOINT PREDICTION OF JAVA HEAP USAGE

摘要

Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

主权项

1. A computer-implemented method comprising:
extracting a particular feature vector from at least one of: JVM heap size, histogram, seasonal factors, linear trends, non-linear trends, level spike, level shift, level drift, variance change, outliers, or endpoint prediction; classifying a state based on the feature vector; identifying a point between two consecutive intervals of two distinct feature vectors, including the particular feature vector, as being an event that marks a state transition; and identifying the state transition with at least one of: (a) an enactment of a directive or (b) an anomaly.