Abstract: Methods and apparatus for user interface navigation may facilitate altering the output of a user interface in response to a predetermined action. Methods and apparatus for user interface navigation may cause a user interface presenting a first resource to present a static and/or dynamically created landing resource upon a user performing a predetermined action. The landing resource may comprise a website, an application, and the like. Methods and apparatus for user interface navigation may cause the user interface to present a return resource to the user upon the user performing the same or different predetermined action. In some embodiments, the return resource may comprise the first resource.

Abstract: Preconditioning for stochastic simulation of computer system performance is described. In an embodiment, methods taught herein include preconditioning a performance scenario that is simulated as part of a software deployment. The performance scenario specifies devices included as part of a hardware configuration supporting the software. The performance scenario can be modified based, at least in part, on the result of the preconditioning. Other methods taught herein include two complementary techniques for preconditioning performance scenarios, referred to as pseudo-simulation and workload aggregation.

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: Simulating an application. A method that may be practiced in a computing environment configured for simulating an application modeled by an application model deployed in a performance scenario of a computing system by deploying service models of the application model to device models modeling devices. The method includes referencing a performance scenario to obtain a transaction being modeled as originating from a first device model. The transaction invokes of a first service model. The first service model specifies hardware actions for simulation. The first service model is referenced to determine the hardware actions for simulation and the next referenced service. The next referenced service specifies hardware actions to be added to the transaction and may specify invocation of other service models. A chain of hardware actions is generated by following the invocation path of the service models. The hardware actions are applied to device models to simulate the transaction.

Abstract: Validating simulation models. A computing environment includes a performance scenario of a system. The performance scenario includes device models defining device behavior and/or capacity. The performance scenario further includes interconnections between one or more device models. A static model analysis of the system is performed. The static model analysis analyzes at least one of configuration of device models defined by the performance scenario or interconnection of device models defined by the performance scenario. A static capacity analysis to analyze device model limitations as they relate to statically defined performance scenario characteristics is performed. An application constraints validation can be performed. This includes comparing the performance scenario to software deployment best practices and rules related to models similar to the performance scenario. A simulation runtime validation may also be performed to evaluate dynamic device usage and latencies to simulate the system.

Abstract: A performance prediction simulator gives effect to the resource contention among multiple resources in a simulated system by adjusting event durations appropriately. A resource topology tree defining the resource configuration of the system is input to the simulator. The simulator includes an evaluation engine that determines the amount of resource used during each simulation interval of the simulation and records the resource usage in a resource contention timeline, which can be displayed to a user. The amount of resource used during a simulation is also used to adjust the event duration calculations of the hardware models associated with each event.

Abstract: A computing system for determining performance factors for using in performance modeling of a deployed subject system, is presented. The computing system includes a plurality of software components comprising the subject system. Each of the components is susceptible to event tracing while executing on the computing system. The computing system includes a tracing component. The tracing component is configured to trace events of the components of the subject system as they execute. The computing system includes a transaction identification table. The transaction identification table comprises starting and ending actions for transactions performed by the subject system. The computing system also includes a transaction identification component that identifies actions from traced events, identifies related actions corresponding to a transaction according to the starting and ending actions in the transaction identification table, and stores the related actions in the transaction workflow data store.

Abstract: A prescribed system architecture is recommended to an entity that desires to implement a system supporting distributed applications. A performance scenario is created based on anticipated usage, devices employed by servers running the distributed applications, and topology of locations using the servers. An optimized scenario may be provided by determining device optimization, different use load, and if possible consolidation of distributed applications on servers.

Abstract: Preconditioning for stochastic simulation of computer system performance is described. In an embodiment, methods taught herein include preconditioning a performance scenario that is simulated as part of a software deployment. The performance scenario specifies devices included as part of a hardware configuration supporting the software. The performance scenario can be modified based, at least in part, on the result of the preconditioning. Other methods taught herein include two complementary techniques for preconditioning performance scenarios, referred to as pseudo-simulation and workload aggregation.

Abstract: Model-based capacity planning includes accessing a model of a planned system that includes multiple components. Relationships among the multiple components are identified based on the model of the system. Transactions to be performed by the planned system are identified along with a cost associated with each of the identified transactions. Operation of the planned system is simulated using the model of the planned system and the identified costs.

Abstract: A performance prediction simulator gives effect to the resource contention among multiple resources in a simulated system by adjusting event durations appropriately. A resource topology tree defining the resource configuration of the system is input to the simulator. The simulator includes an evaluation engine that determines the amount of resource used during each simulation interval of the simulation and records the resource usage in a resource contention timeline, which can be displayed to a user. The amount of resource used during a simulation is also used to adjust the event duration calculations of the hardware models associated with each event.

Abstract: A performance prediction simulator gives effect to the resource contention among multiple resources in a simulated system by adjusting event durations appropriately. A resource topology tree defining the resource configuration of the system is input to the simulator. The simulator includes an evaluation engine that determines the amount of resource used during each simulation interval of the simulation and records the resource usage in a resource contention timeline, which can be displayed to a user. The amount of resource used during a simulation is also used to adjust the event duration calculations of the hardware models associated with each event.

Abstract: The described systems, methods, and data structures are directed at automatic configuration of transaction-based performance models. Models of an infrastructure are created and automatically configured using data provided by existing management tools that are designed to monitor the infrastructure. These automatically configured models may be used to simulate the performance of the infrastructure in the current configuration or other potential configurations. The models may also be automatically validated calibrated by comparing results from simulations and measured data.

Abstract: A model of a system is generated and used as a basis for managing the system. As the system is managed, the system model can be updated to reflect changes to the system. Managing of the system can include one or more of provisioning applications in the system, provisioning applications in virtual systems, provisioning test environments, monitoring the configuration of the system, monitoring the system including the health of the system, performing capacity planning for the system, and propagating attributes to different components in the system.

Abstract: The described systems, methods, and data structures are directed at automatic configuration of transaction-based performance models. Models of an infrastructure are created and automatically configured using data provided by existing management tools that are designed to monitor the infrastructure. These automatically configured models may be used to simulate the performance of the infrastructure in the current configuration or other potential configurations.