Abstract: A capacity planning system (100, 200), comprising a memory device (112, 212) storing a number of historical input data files (115, 215), a number of input data configuration files (120, 220), and a number of simulator configuration files (125, 225), a processor (130, 230) communicatively coupled to the memory device (112, 212), a decision policy module (110, 210) communicatively coupled to the processor (130, 230) that creates a number of decision policies based on an environment in which the system (100, 200) is operating and the historical input data files (115, 215), and a simulator (135, 235) that executes the number of decision policies based on the data provided to it by the processor (130, 230) and provides a dynamically evolving model of an environment as a number of resources defined in the number of simulator configuration files (125, 225) move through the environment.

Abstract: A method comprises receiving, by a processor, a description of a later version of a service model and determining, by the processor, backward compatibility of the description of the later version of the service model with a description of a previous version of the service model. Each service model comprises one or more versionable types and each versionable type has an associated description.

Abstract: A computer-based solver provides a method of assigning computing resources in a data center to meet computing resource requirements of an application. The solver initially creates a list of application components wherein each application component represents a largest possible combination of shared resource requirements from the application. Next, the solver identifies a set of eligible resource servers with each resource server capable of fulfilling the resource requirements for each application component in the list of application components. Typically, the resource requirements of either the shared or discrete are met by a resource server with sufficient capacity. If there is at least one feasible solution, the solver then matches an optimal combination of resource servers to each application component in the list of application components using a genetic algorithm (GA).