Oracle International Corporation

Samir Sebbah

Claire M Bagley

Michael Patrick Colena

Serdar Kadioglu

Gene Gleyzer

Mirza Mohsin Beg

Techniques for determining one or more replication paths for resources in different failure domains, while maintaining a target resiliency level, are disclosed. A replication path is a sequence of at least a subset of the set of resources. Based on the sequence, a resource is selected for replicating at least a portion of the resource consumers corresponding to each of the subset of resources. A replication path may be determined by adding a resource to the replication path and/or replacing one resource with another resource to modify the replication path. The modified replication path maintains the target resiliency level if the modified replication path does not include any loop of a length less than or equal to the target resiliency level.

Determining a replication path for resources of different failure domains

Systems, methods, and other embodiments are disclosed that use constraint programming to dynamically distribute cache throughout a data grid. In one embodiment, modeling logic is configured to generate a constraint programming model based at least in part on a set of constraints that limits assignment of a plurality of data partitions to a plurality of virtual machines hosted on a plurality of physical machines in a data grid. Solver logic is configured to generate at least one solution for assigning the plurality of data partitions to the plurality of virtual machines by operating on the constraint programming model using constraint programming techniques.

System and method for dynamic cache distribution for in-memory data grids

Techniques for selecting test configurations associated with a particular coverage strength using a constraint solver are disclosed. A set of parameters are configurable for conducting a test on a particular target application. A data model generator identifies one or more candidate test configurations based on the set of parameters. The data model generator determines a set of interactions based on a desired coverage strength. The data model generator specifies a selection variable indicating the candidate test configurations that are selected for testing the particular target application. The data model generator specifies constraint(s) minimizing the number of selected test configurations. The data model generator specifies constraint(s) requiring that each interaction be covered by at least one selected test configuration. The data model, including the selection variable and the constraints, are input to a constraint solver to obtain a set of selected test configurations associated with the desired coverage strength.

Selecting a set of test configurations associated with a particular coverage strength using a constraint solver

A system and method of assigning resource consumers to resources using constraint programming is disclosed. The resource consumers are to be assigned to the resources in a way that achieves one or more particular objectives, such as resiliency, load balancing, and network embedding criteria. A data model, for application to a constraint programming solver, formulates the assignment problem as a set of constraints for a solution to be found. The data model includes a first set of data model elements corresponding to pairs of resources, each pair having two resources in different failure domains. The data model includes a second set of data model elements corresponding to the resource consumers. The data model includes a constraint that limits assignment of the resource consumers to the resources to: assigning the resource consumers respectively to the pairs of resources.

System and method of assigning resource consumers to resources using constraint programming

Techniques for assigning applications to virtual machines (VMs) using constraint programming are disclosed. The applications are associated with application groups. The assignment problem requires that each application of a particular application group be assigned to a VM hosted by a same machine. A data model, for application to a constraint programming solver, formulates the assignment problem as a set of constraints for a solution to be found. The data model includes a set of data model elements corresponding to the applications to be processed. The data model includes a set of data model elements corresponding to the VMs. The data model includes a set of constraints that limits assignment of any particular application to a VM corresponding to a tenant associated with the particular application. The set of constraints further limits assignment of applications corresponding to a same application group to VMs executing on a same machine.

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Medford, MA, United States of America

Samir Sebbah