Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A product configurator comprises a constraint network having a plurality of nodes. The configurator allows a modeler to designate that one of more nodes of a configuration are input-required nodes. During an interactive session, the configurator may receive a request for an auto-completion of the configuration. The configurator will determine if an input has been received for the input-required nodes, and prohibit the auto-completion if the input has not been received.

Abstract: A solver solves constraint satisfaction problems. The solver receives a user decision and propagates the user decision over a constraint satisfaction problem network. The solver then determines if there is a conflict on the network based on the propagation. If there is a conflict, the solver allows a user to resolve the conflict interactively by either canceling the user decision or dropping one or more previously made user decisions.

Abstract: A constraint based product configurator determines a structure that is adapted to be associated with one or more components and determines if an arbitrary association is possible for the structure. If an arbitrary association is possible, the configurator associates a component with the structure and generates a component association choice for the association. The component association choice is an identity of the structure and the corresponding first component.

Abstract: A constraint solver for solving a constraint satisfaction problem model which includes at least one part-whole hierarchy assigns variables to each part of the part-whole hierarchy. The variables include at least one of selected, quantity, and relative quantity, wherein each part has a parent. The solver automatically applies at least one part-whole hierarchy constraint to the part and solves the model and generates a solution.

Abstract: A system for solving a dynamic constraint satisfaction problem comprises a constraint network of variables and constraints. The system creates a first sub-problem model that includes a first model type, one or more first variables and zero or more first constraints. The system propagates the first constraints through the constraint network and determines if a first conflict is detected from propagating the first constraints. If the first conflict is detected, the system restores the constraint network variables to a first previous state before the first constraints were propagated. The system creates a first sub-problem set that includes a second model type and one or more sub-problem models. The system connects the first sub-problem model to the first sub-problem set via a second constraint and propagates the second constraint through the constraint network.

Abstract: An interactive product configurator includes a constraint network having a plurality of nodes. The configurator receives a new user choice after previously asserting one or more default decisions. The configurator then retracts the previously asserted default decisions before asserting the new user choice. The default decisions are then reasserted.

Abstract: A product configurator includes a constraint based network that includes a plurality of nodes. The configurator receives one or more user selections and generates one or more automatic selections to form a complete configuration. The configurator receives a request to change the configuration after forming the complete configuration, and retracts the automatic selections. For each of the retracted automatic selections, the configurator determines if the retracted automatic selection is a configuration-defining selection and reasserts the configuration-defining selections.

Abstract: A constraint solver for solving a constraint satisfaction problem network that comprises a plurality of nodes and a plurality of constraints. The solver receives a request to remove an inferred problem association and determines one or more user decisions that triggered the inferred problem association.

Abstract: Systems and methods for managing floating point variables are described in the present disclosure. According to one example, an embodiment of a method includes analyzing a constraint on a floating point variable in a system that supports both floating point variables and integer variables. The constraint is designed to have the ability to numerically limit the domain of the floating point variable. The method also includes determining whether or not the floating point variable can be handled as an integer variable and converting the floating point variable to a pseudo integer variable when it is determined that the floating point variable can be handled as an integer variable. This conversion of the floating point variable to a pseudo integer variable allows the domain of the floating point variable to be processed as an integer domain.

Abstract: A product configurator is based on a constraint network having a plurality of nodes. The product configurator, when it receives a user choice, determines which nodes in the constraint network are affected by the user choice. For each affected node, the product configurator stores the prior domain state of the node and associates the stored prior domain state with the user choice. Therefore, the nodes can be restored to their prior state if the user choice is later removed for any reason.

Abstract: A dynamic constraint solver for solving a constraint satisfaction problem network that includes a plurality of nodes and a plurality of constraints determines a cardinality for a set of sub-problems. The cardinality is a numeric domain that represents the minimum number of instances in a port. The solver determines if a number of identifiable instances of sub-problems is less than the minimum number of instances. The solver then causes a user interface to display the identifiable instances and a placeholder for each instance that is below the minimum number of instances and is not identifiable.

Abstract: A solver for a constraint satisfaction problem includes a plurality of variables and a plurality of constraints. A floating point variable has a domain and is assigned a value by first determining if a predetermined value can be assigned to the floating point variable if the predetermined value is in the domain. If not, the solver determines if a bound of the domain can be assigned to the floating point variable. If the predetermined value can not be assigned to the floating point variable and the bound of the domain can not be assigned to the floating point variable, the solver assigns a value to the floating point variable using domain splitting.