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: Systems and methods for displaying hierarchical data are described. In an implementation, a method includes processing hierarchical data describing a distributed computing environment. The hierarchical data includes a plurality of levels, in which, at least one of the levels includes a plurality of containers, each of the containers includes one or more items, and each of the items is disposed in another level that is a child of the at least one level. The processed hierarchical data is output for display in a single window on a display device such that the hierarchical data indicates a relationship between each of the items and a respective one of the containers and is configured such that each of the items may be edited without interacting with another window.

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: 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: Availability of a clustered system is determined by identifying all possible states of a cluster, mapping the connections between the states are mapped and determining the probability of the cluster being in each of the states is determined. Availability of the cluster is the sum of the probabilities of being in those states in which the cluster is at least partially available. Parameters affecting the availability of the cluster include the rates at which a cluster transitions from state to state, including the reciprocal of Mean Time To Fail, Mean Time to Fail-Over, Mean Time To Fail-Back and Mean Time To Restore. Availability of statefull and stateless clusters may be determined. Availability of clusters including any number of nodes (e.g., a Internet service) can be determined. In one embodiment of the invention, a target availability is determined and parameters are varied to determine the most efficient approach to reach the target availability.

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.