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: 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: 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 one aspect, a method of instructing at least one operator in a best practices implementation of a process for managing resource capacity in an information technology (IT) environment is provided.

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: Scheduling system resources. A system resource scheduling policy for scheduling operations within a workload is accessed. The policy is specified on a workload basis such that the policy is specific to the workload. System resources are reserved for the workload as specified by the policy. Reservations may be hierarchical in nature where workloads are also hierarchically arranged. Further, dispatching mechanisms for dispatching workloads to system resources may be implemented independent from policies. Feedback regarding system resource use may be used to determine policy selection for controlling dispatch mechanisms.

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 present invention extends to methods, systems, and computer program products for abstracting an operating environment from an operating system running in the operating environment. Within an operating environment, an operating environment abstraction layer abstracts and exposes operating environment resources to an operating system. Accordingly, appropriately configured operating environment abstraction layers provide the operating system with a uniform interface to available resources across a variety of different operating environments. Each operating environment abstraction layer and the operating system include adjustable algorithms that can be adjusted to appropriately provide services to requesting applications based on exposed resources of the operating environment. Abstraction layers can be configured to analyze and become fully aware of their operating environment, including identifying the presence of other abstraction layers.

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: 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 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: 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 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: 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: 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: 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: 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.