Abstract: A machine learning model is trained to predict short-and long-term resource usage for a user and to take these predictions and forms a provisioning plan for one or more resources, optimized such that resources are started in enough time before they are needed to be used to reduce or eliminate any appreciable delay on the user experience-side but not too much before they are needed that the resource(s) would be idle waiting for usage to occur. If a Gen AI control center is provided that offers a comprehensive user interface displaying the current system state, suggested plans, and prediction quality measures, users can then interact with a chatbot in the Gen AI control center for detailed information on selected plans, and manually add events to anticipate demand spikes. The Gen AI control center is able to create explanatory text and diagrams, improving the understanding of suggested plans.

Abstract: The present disclosure involves systems, software, and computer implemented methods for correlating critical events to identified log data. An example event log analyzer can identify a set of log messages. One or more occurrences of a first critical event and a time of each of the occurrences are identified. One or more candidate subsets of log messages are identified. Each log message in each candidate subset is associated with a timestamp that is within a predefined time window prior to the time of an occurrence of the first critical event. A candidate subset of log messages is selected as a correlator of the first critical event. A rule is defined using the selected candidate subset of log messages. The rule defines a second critical event that correlates to the first critical event. The rule is associated with one or more actions to perform when the second critical event occurs.

Abstract: The present disclosure involves systems, software, and computer implemented methods for correlating critical events to identified log data. An example event log analyzer can identify a set of log messages. One or more occurrences of a first critical event and a time of each of the occurrences are identified. One or more candidate subsets of log messages are identified. Each log message in each candidate subset is associated with a timestamp that is within a predefined time window prior to the time of an occurrence of the first critical event. A candidate subset of log messages is selected as a correlator of the first critical event. A rule is defined using the selected candidate subset of log messages. The rule defines a second critical event that correlates to the first critical event. The rule is associated with one or more actions to perform when the second critical event occurs.