Abstract: In accordance with certain aspects of the model-based policy application, each of a plurality of policies is associated with appropriate parts of a model of a heterogeneous system. A deployment agent is invoked to apply each of the plurality of policies to components associated with the parts of the model. An identification of a change to one of the plurality of policies is received, and the deployment agent is also invoked to apply the changed policy to selected ones of the components associated with the parts of the model.

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: Model-based provisioning of test environments includes accessing a model of an application to be installed in a test environment of a system and further accessing a model of the system and a model of the test environment. An installation specification for the application is also generated, the installation specification being derived at least in part from the model of the application, the model of the system, and the model of the test environment.

Abstract: A server allocation controller provides an improved distributed data processing system for facilitating dynamic allocation of computing resources. The server allocation controller supports transaction and parallel services across multiple data centers enabling dynamic allocation of computing resources based on the current workload and service level agreements. The server allocation controller provides a method for dynamic re-partitioning of the workload to handle workload surges. Computing resources are dynamically assigned among transaction and parallel application classes, based on the current and predicted workload. Based on a service level agreement, the server allocation controller monitors and predicts the load on the system. If the current or predicted load cannot be handled with the current system configuration the server allocation controller determines additional resources needed to handle the current or predicted workload. The server cluster is reconfigured to meet the service level agreement.