Abstract: Automated or autonomic techniques for managing deployment of one or more resources in a computing environment based on varying workload levels. The automated techniques may comprise predicting a future workload level based on data associated with the computing environment. Then, an estimation is performed to determine whether a current resource deployment is insufficient, sufficient, or overly sufficient to satisfy the future workload level. Then, one or more actions are caused to be taken when the current resource deployment is estimated to be insufficient or overly sufficient to satisfy the future workload level. Actions may comprise resource provisioning, resource tuning and/or admission control.

Abstract: Automated or autonomic techniques for managing deployment of one or more resources in a computing environment based on varying workload levels. The automated techniques may comprise predicting a future workload level based on data associated with the computing environment. Then, an estimation is performed to determine whether a current resource deployment is insufficient, sufficient, or overly sufficient to satisfy the future workload level. Then, one or more actions are caused to be taken when the current resource deployment is estimated to be insufficient or overly sufficient to satisfy the future workload level. Actions may comprise resource provisioning, resource tuning and/or admission control.

Abstract: A method, apparatus, and computer instructions for updating a software component. A determination is made as to whether to update the software component. The software component is updated to form an updated software component in response to a determination to update the software component. The updated software component is automatically tested. A corrective action is performed in response to a failure in testing of the updated component.

Abstract: Techniques for performing adaptive and robust prediction. Prediction techniques are adaptive in that they use a minimal amount of historical data to make predictions, the amount of data being selectable. The techniques are able to learn quickly about changes in the workload traffic pattern and make predictions, based on such learning, that are useful for proactive response to workload changes. To counter the increased variability in the prediction as a result of using minimal history, robustness is improved by checking model stability at every time interval and revising the model structure as needed to meet designated stability criteria. Furthermore, the short term prediction techniques can be used in conjunction with a long term forecaster.

Abstract: Automated or autonomic techniques for managing deployment of one or more resources in a computing environment based on varying workload levels. The automated techniques may comprise predicting a future workload level based on data associated with the computing environment. Then, an estimation is performed to determine whether a current resource deployment is insufficient, sufficient, or overly sufficient to satisfy the future workload level. Then, one or more actions are caused to be taken when the current resource deployment is estimated to be insufficient or overly sufficient to satisfy the future workload level. Actions may comprise resource provisioning, resource tuning and/or admission control.

Abstract: Techniques for performing adaptive and robust prediction. Prediction techniques are adaptive in that they use a minimal amount of historical data to make predictions, the amount of data being selectable. The techniques are able to learn quickly about changes in the workload traffic pattern and make predictions, based on such learning, that are useful for proactive response to workload changes. To counter the increased variability in the prediction as a result of using minimal history, robustness is improved by checking model stability at every time interval and revising the model structure as needed to meet designated stability criteria. Furthermore, the short term prediction techniques can be used in conjunction with a long term forecaster.

Abstract: A method, apparatus, and computer instructions for updating a software component. A determination is made as to whether to update the software component. The software component is updated to form an updated software component in response to a determination to update the software component. The updated software component is automatically tested. A corrective action is performed in response to a failure in testing of the updated component.

Abstract: A method, computer program product, and data processing system for constructing a self-managing distributed computing system comprised of “autonomic elements” is disclosed. An autonomic element provides a set of services, and may provide them to other autonomic elements. Relationships between autonomic elements include the providing and consuming of such services. These relationships are “late bound,” in the sense that they can be made during the operation of the system rather than when parts of the system are implemented or deployed. They are dynamic, in the sense that relationships can begin, end, and change over time. They are negotiated, in the sense that they are arrived at by a process of mutual communication between the elements that establish the relationship.