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: Simulating network connections. A method includes generating a transaction by simulating a method model of a service model. The transaction includes representations of network interactions. A sequence of actions is created. The actions define network hardware activities including network actions performed by one or more source computer models, one or more network models, and one or more destination computer models. The sequence of actions is applied to network hardware device models to simulate network connectivity.

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: Modeling storage devices. One or more data structures define one or more storage devices including empirical characterizations or other characteristics of storage device operations for the specific storage devices. The empirical characterization are obtained as a result of laboratory testing of one or more sample components of the specific storage devices, or storage device similar to the specific storage devices. Complex storage device models that include disk arrays and storage networks can be represented as combinations of element models I/O operations are simulated by applying data structures that represent storage device operations to the one or more data structures. A latency is calculated based on the application of models of I/O operations as storage device operations. The latency may include portions calculated from empirical testing data as well as portions calculated from analytical modeling information.

Abstract: Simulating device interactions. A method may be practiced in a computing system for simulating interconnected devices. The method of simulating device interactions may be done in performing an overall transaction to obtain an output of system performance characteristics including measurement of latencies and/or device loads for actions performed by devices in performing the overall transaction. The method includes dividing a transaction into individual actions. The actions are applied to appropriate device models to produce latencies and/or device utilizations for the action as applied to the appropriate device model. This may be done by including an indication of an action type and optionally an action subservice. Memory resources may be conserved by ending the simulation when latencies and/or device utilizations have settled. Device utilization and latency may be aggregated and averaged over time.

Abstract: Embodiments relate to determining a value of a type of performance parameter of a target device configuration that has known values of various types of configuration attributes. Reference device configurations can be obtained that respectively having known values for types of configuration attributes corresponding to the types of configuration attributes of the target device and respectively having known values of the type of performance parameter whose value is to be determined for the target device. The performance parameter values of the reference device configurations can be weighted based on the reference device configurations' respective distances from the target device configuration in a space defined by the types of configuration attributes, where the types of configuration attributes correspond to respective dimensions of the space. The weighted performance parameter values of the reference device configurations can be used to determine the performance parameter value of the target device.

Abstract: In an implementation, a system includes a simulation engine that is executable to simulate actions performed by a plurality of devices in a distributed system. The system also includes a plurality of pluggable device models that are accessible by the simulation engine via an interface. Each of the device models represents one of the devices and is configured to map a cost of performing at least one of the actions to an action latency by the corresponding device.

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: 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.