Microsoft Technology Licensing, LLC

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Jonathan C Hardwick

Efstathios Papaefstathiou

Pavel Anatolievich Dournov

John Morgan Oslake

Kevin John Savage

David Edward Guimbellot

Galen Clyde Hunt

Robert M Fries

Robert Victor Welland

Bassam Tabbara

Ashvinkumar J Sanghvi

Rene Antonio Vega

Aamer Hydrie

Anders Bertil Vinberg

Geoffrey Hugh Outhred

Raymond W McCollum

John M Parchem

Vij Rajarajan

David C James

Rob M Mensching

Vitaly Voloshin

Michael Joseph Healy

Bruce Wayne Copeland

Paul Barham

John H Wilson

Mazhar Naveed Mohammed

Glenn R Peterson

Rebecca Isaacs

Eric Joseph Winner

Anand Lakshminarayanan

Rajagopalan Badri Narayanan

Jeffrey A Woolsey

Edwin R Lassettre

Zhengkai Kenneth Pan

Kevin D J Grealish

Quanzhan Zheng

Glenn F LaVigne

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.

Dynamic software performance models

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.

Simulation using resource models

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.

Prescriptive architecture recommendations

Resource model training

Creation of resource models

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.

Generating static performance modeling factors in a deployed system

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.

Model-based policy application

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.

Evaluating hardware models having resource contention

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.

Preconditioning for stochastic simulation of computer system performance

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Kirkland, WA, United States of America

Jonathan C Hardwick