395 results about "Resource allocation systems" patented technology

Disclosed herein are representative embodiments of methods, apparatus, and systems for distributing a resource (such as electricity) using a resource allocation system. In one exemplary embodiment, a plurality of requests for electricity are received from a plurality of end-use consumers. The requests indicate a requested quantity of electricity and a consumer-requested index value indicative of a maximum price a respective end-use consumer will pay for the requested quantity of electricity. A plurality of offers for supplying electricity are received from a plurality of resource suppliers. The offers indicate an offered quantity of electricity and a supplier-requested index value indicative of a minimum price for which a respective supplier will produce the offered quantity of electricity. A dispatched index value is computed at which electricity is to be supplied based at least in part on the consumer-requested index values and the supplier-requested index values.

A network resource allocation system and method of the same are provided according to the present invention. The system and method are applicable to a wireless mesh network, the method including: programming traffic of connections between routers; exchanging data between neighboring routers, thereby enabling the routers to perform dispersive network resource allocation according to the exchanged data; checking a local time window and a global time window for expiration by the router; and, under the condition of local performance deterioration or local traffic variation, activating a local routing adjustment mechanism by the router, wherein, if global performance deterioration currently exists, then activating a global optimization mechanism by a central controller, and, if not, then resetting the global time window. Accordingly, the present invention is capable of perceiving the traffic status of each connection between routers, thereby efficiently directing traffic to various channels and wireless transceivers.

The invention relates to a resource allocation system in a private cloud environment. The system comprises a global policy controller, cloud application policy controllers and group policy controllers, wherein a resource manager interacts with a resource exploring proxy, receives index data, judges whether an alarm is generated for the index data or not according to alarm conditions, and if so, submits the alarm to a policy control network; the group policy controller first processes the alarm, generates a resource adjusting plan if the alarm can be processed, and reports the alarm to the cloud application policy controller if the alarm cannot be processed; the cloud application policy controller processes the received alarm, generates the resource adjusting plan if the alarm can be processed, and reports the alarm to the global policy controller if the alarm cannot be processed; the global policy controller processes the received alarm and generates the resource adjusting plan; the policy control network returns the resource adjusting plan to the resource manager; and the resource manager executes the resource adjusting plan. By constructing graded control policies and assisting coordination mechanisms among the control policies in different grades, the invention effectively improves the utilization rate of private cloud resource.

Systems, methods, apparatus, and computer-readable media provide for allocating vehicle resources to future vehicle requirements based on priority environments, such as in or near ‘hot spots,’ or other priority locations, where requests for vehicle resources are or are expected to be particularly high. In one embodiment, a vehicle resource allocation system allocates vehicles to requested locations such that vehicles of a fleet become more readily available to a hot spot, without requiring vehicles to be directed specifically to the hot spot.

The invention provides a distributed computing resource allocation system and a task processing method. The distributed computing resource allocation system comprises a central server and a plurality of node servers. The central server is used for receiving video image tasks sent by a user and allocating the video image tasks to corresponding node servers through central ends of computing units based on the types of the video image tasks as well as the current task number and load conditions of the node servers. The node servers are used for processing the video image tasks through node ends of the computing units corresponding to the video image tasks. According to the distributed computing resource allocation system and the task processing method provided by the invention, different types of video image tasks can be processed by using multiple servers of one server cluster, and the utilization rate of the server cluster can be increased.

Described is a Distributed Resource Allocation System (DRAS) for sensor control and planning. The DRAS comprises an information framework module that is configured to specify performance goals, assess current performance state, and includes sensor models to achieve the performance goals. The DRAS is configured to further allocate the sensors to achieve the performance goals. Once allocated, the DRAS then reassesses the current performance state and continues reallocating the sensors until the current performance state is most similar to the performance goals.

The present invention provides a method and apparatus for resource allocation in a system which includes work items which are to be completed in both business time and real time. Work items are placed in a delta queue which has a calendar associated therewith indicating business time and non-business time for resources associated with the delta queue. When the calendar associated with the delta queue enters into a non-business time, a scheduler pushes an item at the head of the delta queue which corresponds to the amount of non-business time. Work items in the delta queue are thus delayed by the appropriate amount of time corresponding to the non-business time.

The present disclosure relates to scenario-management systems for use in resource-allocation systems. In an exemplary embodiment, a scenario editor comprises means for creating, editing, and storing business rules, rule groups, and in a database; and means for creating links among selected business rules, rule groups, and scenarios, such that changes to a first business rule, rule group, or scenario are reflected in a second business rule, rule group, or scenario linked to the first. In further embodiments, the scenario editor further comprises means for associating a scenario with a particular time period. In a further exemplary embodiment, a scenario-management system comprises a central database of business rules, and a processor configured to identify an active scenario, create a list of rule IDs corresponding to the business rules associated with the active scenario, and pass the list of rule IDs to a resource-allocation module.

A system and method for distributing a collection of physical (tangible) resources, e.g., books, newspapers, periodicals, trade publications, compact discs, equipment, video tapes microfilm. In one embodiment, a method comprises the step of accessing a web server maintaining a database of profiles associated with the collection of resources by a user via a web browser. The user may then issue a search request via the web browser to search for one or more resources within the collection of resources. Upon identifying one or more resources from the search, the user may contact one or more owners of the one or more resources identified. The user may contact the one or more owners based on the contact information provided in the profiles in the one or more resources identified. Upon contacting the one or more owners, the user may borrow the one or more resources previously identified.

Resource allocation techniques for robust optimization of a set of assets. In these techniques, a user defines or selects scenarios that model investment conditions including normal and/or extreme conditions. The set of assets is optimized across the scenarios to produce weights for the assets in the set that optimize the worst-case value of the assets. A resource allocation system is disclosed which first selects a reliable set of assets for optimization and then optimizes the reliable set of assets. Optimization of the set of assets may involve robust or non-robust optimization, many different kinds of constraints and/or multiple constraints, different objective functions, and different adjustments for the objective functions. Selection of the set of assets and selection of the kind of optimization, of the constraints, of the objective function, and of the adjustments to the objective function is done using a graphical user interface.

The invention belongs to the technical field of vehicle-mounted communication, and discloses an Internet of Vehicles content edge unloading method and a mobile resource allocation system. Resource information of a vehicle node is managed, tasks are divided, a target node is combined, and thus, calculation unloading in a vehicular Ad Hoc network is realized; a calculation unloading target node selection method is adopted, in reference to the calculation ability and the time capable of providing service of a node, a corresponding task amount is allocated to the node, and according to the information of each resource, a vehicle is screened. The calculation unloading problem in an interconnection scene between high-mobility vehicles can be solved, and the target of performing calculation at any time and at any place by a user is realized. The resource information of vehicle nodes with high mobility is managed, task dividing and target node selection are combined, and calculation unloadingin the vehicular Ad Hoc network can be realized; and on the basis of comprehensively considering multiple factors, the calculation unloading target node selection method is adopted, and the vehicle isscreened according to the information of each resource.

The present invention provides a method and system for improving memory testing efficiency, raising the speed of memory testing, detecting memory failures occurring at the memory operating frequency, and reducing data reported for redundancy repair analysis. The memory testing system includes a first memory tester extracting failed memory location information from the memory at a higher memory operating frequency, an external memory tester receiving failed memory location information at a lower memory tester frequency, and an interface between the first memory tester and the external memory tester. The memory testing method uses data strobes at the memory tester frequency to clock out failed memory location information obtained at the higher memory operating frequency. In addition, the inventive method reports only enough information to the external memory tester for it to determine row, column and single bit failures repairable with the available redundant resources. The present invention further provides a redundant resource allocation system, which uses a bad location list and an associated bad location list to classify failed memory locations according to a predetermined priority sequence, and allocates redundant resources to repair the failed memory locations according to the priority sequence.

The invention discloses a federated learning computing unloading resource allocation system and a method based on a cloud side end, and aims to make an accurate decision for computing task unloading and resource allocation, eliminate the need for solving a combinatorial optimization problem and greatly reduce the computing complexity. Based on cloud side three-layer federated learning, the adjacent advantage of edge nodes to a terminal is comprehensively utilized, core powerful computing resources in cloud computing are also utilized, the problem that the computing resources of the edge nodes are insufficient is solved, a local model is trained at each of multiple clients to predict an unloading task. A global model is formed by periodically executing one-time parameter aggregation at an edge end, the cloud end executes one-time aggregation after the edge executes the periodic aggregation until a global BiLSTM model is formed through convergence, and the global model can intelligently predict the information amount of each unloading task. Therefore, guidance is better provided for calculation unloading and resource allocation.

A telecommunication system includes telecommunication resources, points, and a connection manager. The connection manager is configured to establish connections between the telecommunications resources and the points in response to a request from an application. The connection manager uses routing tables to select the most efficient route from the available routes between a telecommunications resource and a point. The routing tables establish the connection by sending a request to allocate a telecommunications resource to a session manager.

A computing resource allocation system allocates hardware and software resources among employees, based upon a combination of the employee level, job function, and demonstrated workstation performance within the context of the job requirements of the employee and usage patterns of the computing resource. The system collects various performance data for computing resources. A set of policy rules is applied to the collected performance data and processed by the present system. Consequently, the present system automatically identifies and prioritizes employees in need of technology upgrades and replacements based on business needs and available resources. Performance data of a computing resource is captured and transmitted to a central collection agency. From the performance data, the present system determines when partial upgrades, such as memory additions or faster adapters are appropriate based on system performance or errors. In addition, the present system determines when a computing resource experiences continuous performance problems.