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 method comprising:
receiving, at a computer system, a plurality of thread dumps associated within a Java Virtual Machine; identifying, by the computer system, a class of threads within the plurality of thread dumps; calculating, by the computer system, a plurality of thread intensities for the class of threads, each of the plurality of thread intensities corresponding to one of the plurality of thread dumps; determining, by the computer system, one or more trends for the class of threads, based on the plurality of thread intensities for the class of threads; and determining, by the computer system, that the Java Virtual Machine has a memory leak, based on the one or more trends for the class of threads.