Abstract: Configuring a node. A method includes determining a current state of a target node. The current state of the target node refers to objects currently deployed or running in the remote target node. It includes at least one of an operating system, one or more applications, or configuration setting at the target node. The method further includes determining a desired state for the target node to change the current state. The method further includes accessing a dependency graph based on the version of an agent running on the target node. The dependency graph is supplied to a state machine. The state machine is particular to the target node. The state machine using the current state of the target node, the desired state and the dependency graph, performs operations to drive the target node toward its goal.

Abstract: A distributed application may be updated by using information provided by the application to determine grouping, sequencing, and whether or not to advance an update sequence. The application may provide input to an update sequence, and may cause the update sequence to repair one or more replicas prior to updating. The update mechanism may be used to change the distributed application topology as well as make changes to the application executable code, application configuration, quest operating system, virtual machine, and host systems, among others. In some cases, the distributed application may use a quorum to determine which version is current, and the quorum may change during the update process.

Abstract: The present invention discloses methods and systems for providing secure user access to services offered by a service provider to a client application over a network. One embodiment includes receiving an application cookie from the client application and populating a service cookie based on information in the application cookie. Information in the service cookie is utilized as a basis for regulating a provision of services to the client application.

Abstract: Subject matter described herein is directed to reallocating an application component from a faulty data-center resource to a non-faulty data-center resource. Background monitors identify data-center resources that are faulty and schedule migration of application components from the faulty data-center resources to non-faulty data-center resources. Migration is carried out in an automatic manner that allows an application to remain available. Thresholds are in place to control a rate of migration, as well as, detect when resource failure might be resulting from data-center-wide processes or from an application failure.

Abstract: The present invention discloses methods and systems for providing secure user access to services offered by a service provider to a client application over a network. One embodiment includes receiving an application cookie from the client application and populating a service cookie based on information in the application cookie. Information in the service cookie is utilized as a basis for regulating a provision of services to the client application.

Abstract: Methods, systems, and computer-readable media for deploying an update to nodes propagated throughout a data center are provided. Launching new upgrade to hosting environment residing on the nodes typically invokes a mechanism (e.g., fabric controller) to form a group of nodes that are independent of one another with respect to upgrade domains, which are assigned to tenants (e.g., program components of service applications running within the data center) presently hosted by the nodes. The constraints of the update domains are articulated by service level agreements established for the service applications, respectively. Forming the group involves identifying independent nodes for membership, where no two members of the group host analogous tenants (belonging to a common service application) that are assigned to distinct update domains. However, it is acceptable to join to the group those nodes hosting analogous tenants that are each assigned to the same update domain.

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: 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: An authentication protocol is disclosed for use in enhancing the security of communications between software applications and Internet-based service providers. The protocol incorporates a two level authentication model based on a distribution of authentication responsibilities, wherein the application authenticates users and the service provider authenticates the application. Embodiments of the protocol incorporate public key infrastructure and digital certificate technology. Other embodiments of the present invention pertain to applying a corresponding protocol to peer-to-peer communication scenarios.

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: Data replication between a headquarters application and one or more client applications is provided. The client applications are generally located remote from the headquarters application and communicate therewith via known methods. A pair of classes is used to generically transfer data selected for replication. New entity types and/or data types can be synchronized without having to rewrite the synchronization application by simply deploying an appropriate data class and manager class for the new entity. Moreover, the prior art worksheet is abstracted, in some aspects, to a simpler form that is more user friendly.

Abstract: A method and system for defining performance models of distributed applications such as distributed systems or network systems in a way that combines discrete and analytical models and simulating such performance models for analyzing software performance and impacts on devices of the distributed applications is described. Also described is a method for accelerating the simulation process by converting the discrete load into aggregate load dynamically based on the statistical analysis of the simulation results.

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.