Abstract: A method includes displaying a plurality of user modifiable utilization views of a monitored software application, where each of the plurality of user modifiable utilization views is customizable to fit a domain of analysis by identifying one or more resource types of multiple resource components. Each of the plurality of user modifiable utilization views is displayed in one more graphical user interfaces. The method includes determining a desired ordering of the one or more resource types within a hierarchy of types and filtering one or more details of the multiple resource components to create a customized view. A user can navigate between each of the plurality of user modifiable utilization views to dynamically construct related target analysis views including filtered details across one or more other domains of analysis.

Abstract: A system is disclosed that includes an interface to receive a resource governance rule applicable to one or more hardware resources supporting a monitored software application. The system includes utilization logic to measure usage of a particular hardware resource by the monitored software application. An available utilization of the particular hardware resource is determined based on the resource governance rule, and a total utilization of the particular hardware resource is also determined. One or more utilization metrics are determined based on historical usage of the particular hardware resource as compared to a governed capacity and a non-governed capacity available to the particular hardware resource. The system also includes recommendation logic to determine a recommended action to be taken with respect to the monitored software application.

Abstract: Aspects of the subject matter described herein relate to modeling and simulating software contention. In aspects, a model declares properties of a scarce software object and models software components that need the scarce software object to execute. A simulator uses the model to set up data structures that track locking activity related to the software object. The simulator simulates the interaction between software components and hardware including seizing and releasing locks on the software object. The simulator may use a function or other similar mechanism to distribute seize and release operations with respect to a lock across multiple instances of the software object. The simulator may use data generated by a simulation to provide an indication of lock objects and hardware utilization, transaction latencies, and other statistics.

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: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

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: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

Abstract: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

Abstract: A system is disclosed that includes an interface to receive a resource governance rule applicable to one or more hardware resources supporting a monitored software application. The system includes utilization logic to measure usage of a particular hardware resource by the monitored software application. An available utilization of the particular hardware resource is determined based on the resource governance rule, and a total utilization of the particular hardware resource is also determined. One or more utilization metrics are determined based on historical usage of the particular hardware resource as compared to a governed capacity and a non-governed capacity available to the particular hardware resource. The system also includes recommendation logic to determine a recommended action to be taken with respect to the monitored software application.

Abstract: Systems and methods for managing database applications are disclosed. A system includes a fabric that identifies a set of data-tier application components. Each of the data-tier application components includes a logical representation of a collection of database elements. The fabric identifies a set of database runtime resources hosting the set of data-tier application components, and the fabric identifies computing resources used by the set of database runtime resources to host the set of data-tier application components. The system also includes a management point to receive a fabric policy. One or more actions of the fabric policy are automatically applied to affected entities identified by the fabric to bring fabric elements into compliance with the fabric policy.

Abstract: A method includes determining a total storage input/output capacity of a set of storage elements available for use by a software application located on an operating system volume of a computer system. The total storage input/output capacity is determined by evaluating storage input/output capacity of the storage elements at a physical device level and at a file level. The method includes measuring a number of input/output storage operations performed on behalf of the software application. The method also includes determining a storage input/output utilization of the software application based on the measured number of input/output storage operations as compared to the total storage input/output capacity of the set of storage elements.

Abstract: Aspects of the subject matter described herein relate to modeling and simulating software contention. In aspects, a model declares properties of a scarce software object and models software components that need the scarce software object to execute. A simulator uses the model to set up data structures that track locking activity related to the software object. The simulator simulates the interaction between software components and hardware including seizing and releasing locks on the software object. The simulator may use a function or other similar mechanism to distribute seize and release operations with respect to a lock across multiple instances of the software object. The simulator may use data generated by a simulation to provide an indication of lock objects and hardware utilization, transaction latencies, and other statistics.

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: 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: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

Abstract: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

Abstract: Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

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.