Oracle International Corporation

Michael Patrick Colena

Claire M Bagley

Gao Chen

Martin P Plotkin

Joyce Ng

Samir Sebbah

Joshua Deen Griffin

Serdar Kadioglu

David J Kulik

Gene Gleyzer

Mirza Mohsin Beg

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Using constraint programming to set resource allocation limitations for allocating resources to consumers

Information about a set of maintenance tasks and time windows includes a cost value per task per time window. Based on the information, a data model generator generates a data model, including task elements; time elements; cost elements; a total cost element; a constraint that requires each task element be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain; a constraint that requires each cost element be assigned a cost value associated with a time window assigned (or to be assigned) to a task element corresponding to the cost element; and a constraint that requires the total cost element be assigned a total cost value that is a sum of the cost values assigned (or to be assigned) to the cost elements. Based on the data model, a CP solver determines a proposed maintenance schedule.

Using constraint programming to obtain a machine maintenance schedule for maintenance tasks

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned to consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Using constraint programming (CP) to obtain a machine maintenance schedule is described. A data model generator generates a CP data model, including a set of task elements representing a set of maintenance tasks, and a set of time elements representing a set of time windows. The CP data model further includes a constraint that requires each task element to be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain. A search directive generator generates a CP search directive, including applying different time window prioritization methods to maintenance tasks for machines having different failure probabilities. One prioritization method may prioritize time windows based on delinquency costs. Another prioritization method may prioritize time windows based on a current maintenance schedule. A CP solver determines a proposed maintenance schedule based on the CP data model and the CP search directive.

Using constraint programming to obtain a machine maintenance schedule for maintaining machines

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

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.

Assigning applications to virtual machines using constraint programming

A data model for application to a constraint programming solver is generated. The data model includes a set of data model elements. A particular data model element corresponds to a particular request. The particular data model element also corresponds to one or more resources that may be assigned to the request. The data model also includes a set of constraints. One or more sort/search algorithms may be applied with the data model to the constraint programming solver. The sort/search algorithms may direct the constraint programming solver to output certain preferred assignments of resources to requests.

System and method of assigning requests to resources using constraint programming

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.

Interactive product configurator with automatic selections

A method of efficiently representing ranged data sets may include storing a data structure representing a first data set. The data structure may include one or more nodes each representing distinct portions of the data set, and the first data set may include a range of multiple values bounded by an upper bound and a lower bound. A first node may represent the range of multiple values by storing the upper bound and the lower bound. The method may also include receiving a second data set to add to the first data set. The intersection of the first data set and the second data may include at least one value. The method may further include altering the one or more nodes in the data structure such that the union of each of the distinct portions of the data set represented by the one or more nodes is equivalent to the union of the first data set and the second data set; and such that the intersection of each of the distinct portions of the data set represented by the one or more nodes is equivalent to an empty data set.

System and method for efficient representation of dynamic ranges of numeric values

Embodiments of the present invention describe using a data structure to represent variable domains in solving a constraint problem. The data structure includes nodes that are configured to represent ranges of values in order to save memory space and processor power. Ranges of values and single values may be both added to and removed from the data structure such that the data structure does not include duplicate values. Operations may include detecting multiple nodes with adjacent or overlapping ranges that may be combined into a single node, and adding or removing all the values in the structure that are beyond a threshold value. In one embodiment the data structure may be a balanced binary tree. Constraint solvers may either add values to the data structure as the values are eliminated from the solution, or they may remove those values.

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.

Dynamic constraint satisfaction problem solver

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.

Interactive product configurator with default decisions

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.

Interactive product configurator that allows modification to automated selections

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.

Growing community of inventors

Hollis, NH, United States of America

Michael Patrick Colena