Abstract: A computer-implemented method includes storing usage of a resource of said computer system as time-stamped resource usage values, comparing said time-stamped resource usage values with predetermined time-stamped performance goal values, assigning a time-stamped priority value to an application that is running based on at least one of said performance goal values, identifying a future workload demand value by applying a time-series analysis algorithm to at least some of said time-stamped resource usage values and a corresponding at least some of said time-stamped performance goal values for said application resulting in workload demand time frames and related amplitudes of said workload demand time frames, and adjusting a dispatch priority value for said application by setting a minimum dispatch priority for said application based on said future workload demand value.

Abstract: Performance predictions in a computing cluster can be provided by sampling and storing historic workload request data of the computing cluster as time-stamped workload values, forecasting an expected total number of workload requests for a defined time interval in the future based on a time-series analysis of the time-stamped workload values, where the time-series analysis detects cyclic and repeating events in the time-stamped workload values. In response to a result of the time-series analysis, training a workload prediction model by using additional data about acyclic events in expected workload requests, where the training applies a statistical regression technique for predicting a future workload demand for the computing cluster, and in response to exceeding a predefined threshold value of the predicted future workload demand, reassigning resources of the computing cluster.

Abstract: A computer-implemented method includes storing usage of a resource of said computer system as time-stamped resource usage values, comparing said time-stamped resource usage values with predetermined time-stamped performance goal values, assigning a time-stamped priority value to an application that is running based on at least one of said performance goal values, identifying a future workload demand value by applying a time-series analysis algorithm to at least some of said time-stamped resource usage values and a corresponding at least some of said time-stamped performance goal values for said application resulting in workload demand time frames and related amplitudes of said workload demand time frames, and adjusting a dispatch priority value for said application by setting a minimum dispatch priority for said application based on said future workload demand value.

Abstract: A method for enhancing a workload manager for a computer system includes sampling and storing usage of a resource of the computer system as resource usage values, comparing said resource usage values with predetermined performance goal values, assigning a time-stamped priority value to an application that is running based on at least one of the performance goal values by the workload manager, retrieving a portion of the resource usage values and a related portion of the performance goal values for the application, identifying a future workload demand value by applying a time-series analysis algorithm to the resource usage values and the performance goal values for the application resulting in workload demand time frames and related amplitudes of the workload demand time frames, and adjusting a dispatch priority value for the application by setting a minimum dispatch priority for the application based on the future workload demand value.

Abstract: A method for enhancing a workload manager for a computer system includes sampling and storing usage of a resource of the computer system as resource usage values, comparing said resource usage values with predetermined performance goal values, assigning a time-stamped priority value to an application that is running based on at least one of the performance goal values by the workload manager, retrieving a portion of the resource usage values and a related portion of the performance goal values for the application, identifying a future workload demand value by applying a time-series analysis algorithm to the resource usage values and the performance goal values for the application resulting in workload demand time frames and related amplitudes of the workload demand time frames, and adjusting a dispatch priority value for the application by setting a minimum dispatch priority for the application based on the future workload demand value.

Abstract: Performance predictions in a computing cluster can be provided by sampling and storing historic workload request data of the computing cluster as time-stamped workload values, forecasting an expected total number of workload requests for a defined time interval in the future based on a time-series analysis of the time-stamped workload values, where the time-series analysis detects cyclic and repeating events in the time-stamped workload values. In response to a result of the time-series analysis, training a workload prediction model by using additional data about acyclic events in expected workload requests, where the training applies a statistical regression technique for predicting a future workload demand for the computing cluster, and in response to exceeding a predefined threshold value of the predicted future workload demand, reassigning resources of the computing cluster.