Abstract: Described systems and techniques provide fast, efficient, and cost-effective techniques for detecting anomalous behaviors of monitored objects. Multiple hashing algorithms, each providing multiple hash bins, may be used to generate a unique hash signature for each of the monitored objects. Metric values characterizing the behavior of the monitored objects may be aggregated within individual ones of the multiple hash bins of each of the multiple hashing algorithms. Then, one or more machine learning models may be trained using the unique hash signatures and their included, aggregated metric values. During subsequent scoring using the trained machine learning model(s), each of the aggregated metric values of each of the hash bins may be scored, and a single or small subset of anomalous objects may be identified.

Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Abstract: A data stream of performance metrics characterizing a technology landscape may be received. From a plurality of performance prediction models and based on the performance metrics, a subset of performance prediction models may be selected. The subset of performance prediction models may be combined into a composite prediction model. The composite prediction model may be loaded into a model processor for scoring against the data stream of performance metrics to obtain a performance prediction for the technology landscape based thereon.

Abstract: A method on mainframe computing system includes determining a current phase of an ordered list of phases, the current phase being one of a plurality of phases in the ordered list of phases and setting a target phase based on the current phase, the ordered list of phases, and a parameter phase when the parameter phase is provided. The method also includes using the target phase and a resource assignment structure to start and stop a plurality of resources that run on the mainframe computing system. At least some of the plurality of resources have an assigned phase in the resource assignment structure. The method also includes repeating the determining, setting, and using, responsive to receiving a phase command.

Abstract: A method on mainframe computing system includes determining a current phase of an ordered list of phases, the current phase being one of a plurality of phases in the ordered list of phases and setting a target phase based on the current phase, the ordered list of phases, and a parameter phase when the parameter phase is provided. The method also includes using the target phase and a resource assignment structure to start and stop a plurality of resources that run on the mainframe computing system. At least some of the plurality of resources have an assigned phase in the resource assignment structure. The method also includes repeating the determining, setting, and using, responsive to receiving a phase command.