489 results about "Load balancing (computing)" patented technology

Runtime connection load balancing of work across connections to a clustered computing system involves the routing of requests for a service, based on the current operational performance of each of the instances that offer the service. A connection is selected from an identified connection pool, to connect to an instance that provides the service for routing a work request. The operational performance of the instances may be represented by performance information that characterizes the response time and/or the throughput of the service that is provided by a particular instance on a respective node of the system, and is relative to other instances that offer the same service.

A networked computing system including multiple network base stations, user equipment, a network resource controller (NRC), and a data communications network facilitating communications amongst all devices of the networked computing system. The NRC determines a current radio channel available capacity based on a user load associated with regional user equipment, and then forecasts a maximum radio channel capacity based on the current radio channel available capacity. The NRC may be a network base station and it may determine a number of additional user equipment it can support as a component of the forecast maximum radio channel capacity. The NRC/base station may be further configured to determine a handover threshold utilizing the forecast maximum radio channel capacity, and when the NRC base station's number of users exceeds the handover threshold, one or more user equipment may be handed over to a second network base station with better service capacity.

Techniques for fast recovery and/or balancing of connections to a clustered computing system provide management of such connections by determining a number of connections to load balance across nodes and by triggering creation of such connections. In one aspect, a notification of an event regarding the clustered computing system is received by a connection pool manager, a pool of connections to the system is identified based on the notification, and one or more connections from the pool are processed in response to the event. According to an embodiment, the notification comprises the identities of the service, database, server instance and machine that correspond to the event.

Some embodiments provide an elastic architecture for providing a service in a computing system. To perform a service on the data messages, the service architecture uses a service node (SN) group that includes one primary service node (PSN) and zero or more secondary service nodes (SSNs). The service can be performed on a data message by either the PSN or one of the SSN. However, in addition to performing the service, the PSN also performs a load balancing operation that assesses the load on each service node (i.e., on the PSN or each SSN), and based on this assessment, has the data messages distributed to the service node(s) in its SN group. Based on the assessed load, the PSN in some embodiments also has one or more SSNs added to or removed from its SN group. To add or remove an SSN to or from the service node group, the PSN in some embodiments directs a set of controllers to add (e.g., instantiate or allocate) or remove the SSN to or from the SN group. Also, to assess the load on the service nodes, the PSN in some embodiments receives message load data from the controller set, which collects such data from each service node. In other embodiments, the PSN receives such load data directly from the SSNs.

The invention discloses a method for partitioning dynamic tasks of CPU and GPU based on load balance, aiming at solving the technical problem of realizing the load balance between CPU and GPU and among multiple cores of CPU. The technical scheme of the method is as follows: establishing two databases for the task partitioning between CPU and GPU and among multiple cores of CPU; recording the partitioning proportion of tasks, wherein the partitioning proportion of tasks between CPU and GPU is determined according to problem scales; automatically adjusting the partitioning proportion of tasks in the whole executing processes of an application according to the actual measurement performance of CPU and GPU, thus realizing the load balance of task partitioning. The adoption of the method can ensure the load balance of tasks of CPU and GPU, improve the performance that a scientific computing program utilizes the GPU to accelerate, balance the task load among all the cores of CPU, and improve the performance of the multi-core CPU.

The invention provides a network virtual disk file safety management method based on cloud computing, which achieves the functions of automatic load balancing and transparent space expanding and shrinking by adopting cloud computing thought. In order to overcome the defect that public cloud can not realize data safety isolation and authorization use, the method can take Hadoop cloud platform as a base to construct a seamless virtual disk transparent encryption environment and realize distribution storage, data isolation, safe data sharing and other functions.

A method, schema, and computer-readable medium provide various means for load balancing computing devices in a multi-server environment. The method, schema, and computer-readable medium for load balancing computing devices in a multi-server environment may be utilized in a networked server environment, implementing a spreadsheet application for manipulating a workbook, for example. The method, schema, and computer-readable medium operate to load balance computing devices in a multi-server environment including determining whether a file, such as a spreadsheet application workbook, resides in the cache of a particular server, such as a calculation server. Upon meeting certain conditions, the user request may be directed to the particular server.

The invention discloses a cloud computing implementation method. By multilayer load balancing architecture design, a plurality of virtual machines with totally same functions are deployed in an application layer, a middle layer and a database layer, and fault monitoring modules are respectively deployed on the virtual machines; when one virtual machine has a file system read-only fault and the like, the fault monitoring module automatically turns off the virtual machine to realize automatic isolation of a virtual machine node, and reports the fault information to a management center; the management center migrates the access request of the virtual machine having the fault to the other corresponding virtual machine; and the fault or the deletion of any main virtual machine does not cause pause on the current service system, so that the fault of a single main virtual machine can be prevented from affecting the whole cloud computing platform, the smoothness of services is ensured, and the user experience and the performance of the whole system are improved. The invention also discloses a cloud computing system.

The invention provides a decision method and a control module facing to cloud computing virtual machine migration. The control module is composed of a migration trigger module, a virtual machine choosing module and a destination node choosing module. The method includes: firstly setting load threshold value of the system through the migration trigger module, and enabling the system to effectively prevent instant load peak value from triggering unnecessary virtual machine migration by utilizing the load forecast technology; enabling the virtual machine choosing module to choose the virtual machine needing migration according to the migration cost minimum strategy, and reducing the migration cost of the virtual machine to the minimum value to save system resources; and finally, enabling the destination node choosing module to provide the destination node choosing strategy based on weighting probability choosing algorithm. Therefore, the problem of aggregation collision caused by migration of a large number of virtual machines is effectively solved. At last, the decision method can well achieve load balancing and enables resources to be used fair and reasonably.

Disclosed is a method and apparatus for providing range based queries over distributed network nodes. Each of a plurality of distributed network nodes stores at least a portion of a logical index tree. The nodes of the logical index tree are mapped to the network nodes based on a hash function. Load balancing is addressed by replicating the logical index tree nodes in the distributed physical nodes in the network. In one embodiment the logical index tree comprises a plurality of logical nodes for indexing available resources in a grid computing system. The distributed network nodes are broker nodes for assigning grid computing resources to requesting users. Each of the distributed broker nodes stores at least a portion of the logical index tree.

An architecture and design called Resource control programming (RCP), for automating the development of multithreaded applications for computing machines equipped with multiple symmetrical processors and shared memory. The Rcp runtime (0102) provides a special class of configurable software device called Rcp Gate (0600), for managing the inputs and outputs and user functions with a predefined signature called node functions (0500). Each Rcp gate manages one node function, and each node function can have one or more invocations. The inputs and outputs of the node functions are virtualized by means of virtual queues and the real queues are bound to the node function invocations, during execution. Each Rcp gate computes its efficiency during execution, which determines the efficiency at which the node function invocations are running. The Rcp Gate will schedule more node function invocations or throttle the scheduling of the node functions depending on the efficiency of the Rcp gate. Thus automatic load balancing of the node functions is provided without any prior knowledge of the load of the node functions and without computing the time taken by each of the node functions.

The invention provides a load balancing optimization method based on CPU (central processing unit) and MIC (many integrated core) framework processor cooperative computing, and relates to an optimization method for load balancing among computing nodes, between CPU and MIC computing equipment in the computing nodes and among computing cores inside the CPU and MIC computing equipment. The load balancing optimization method specifically includes task partitioning optimization, progress/thread scheduling optimization, thread affinity optimization and the like. The load balancing optimization method is applicable to software optimization of CPU and MIC framework processor cooperative computing, software developers are guided to perform load balancing optimization and modification on software of existing CPU and MIC framework processor cooperative computing modes scientifically, effectively and systematically, maximization of computing resource unitization by the software is realized, and computing efficiency and overall performance of the software are remarkably improved.

The present invention provides a method and a system for a cloud computing system to allocate GPU resources to a virtual machine. By applying the method, the virtual machine does not need to be constantly allocated with certain GPU resources; when the virtual machine needs to perform related high-definition video or 3D rendering, the virtual machine can apply to the cloud computing management system for the GPU resources; after the virtual machine uses the GPU resources, the resources are released and return to a GPU resource pool, so that large amounts of the GPU resources are saved; and the cloud computing management system evenly distributes virtual machines in need of the GPU resources to computing nodes with GPU cards according to created GPU usage identifiers of the virtual machines, thereby achieving usage effects of load balance and GPU resource optimization.

A system and method in which query load balancing process is performed in a cluster of middle-tier computing elements according to the load on at least one, but more preferably a plurality, of the computing elements of the group, this by passing pointers to queries between machines with information or processes pertinent to the query. Optionally the balancing may be carried out in two stages. The first stage performed rapidly, preferably implemented as a hardware device such as a switch for example in which a plurality of queries is distributed to a plurality of computing elements according to at least one descriptor attached to each query or a simple algorithm is used. The second stage preferably performed as above by at least one computing element of the group, but may optionally be performed by a plurality of such elements. Alternatively, the second stage may be performed by a separate computer.

The invention provides a load forecasting method for a cloud computing center host, belongs to the cloud computing field and solves the problem that as virtual machines of different users work on a host of a cloud computing center and the host load is subject to more complicated changes, the host load has to be accurately forecasted to further dispatch the virtual machines so as to achieve the purposes of load balancing and energy consumption reduction. The core of the algorithm lies in that the phase-space reconstruction method in the chaos theory and the data grouping treatment algorithm based on genetic algorithm are combined together. Compared with the present existing method, by adopting the method provided by the invention, a smaller relative error can be obtained. Besides, under the condition that the forecasting time is prolonged, compared with the traditional method, the accumulated error of the forecasting is reduced.

The invention sets forth an approach for aggregating a plurality of NICs in a computing device into a single logical NIC as seen by that computing device's operating system. The combination of the single logical NIC and a network resource manager provides a reliable and persistent interface to the operating system and to the network hardware, thereby improving the reliability and ease-of-configuration of the computing device. The invention also may improve communications security by supporting the 802.1X and the 802.1Q networking standards.

The invention provides a dynamic resource allocation method and system under a space-based cloud computing architecture and a storage medium. The method comprises the following steps: sensing a stateparameter of a current network to generate a network state sensing result; according to the network state sensing result, detecting the traffic condition of each node and link, the user page view andthe traffic consumption of each application under the current network topology structure, and carrying out classification identification on the traffic to generate a traffic classification identification result; predicting a traffic condition based on the geographic position, the time distribution and the access density according to the traffic classification and identification result, and generating a traffic prediction result; and visually generating a resource allocation scheme through a big data analysis platform according to the flow prediction result. Heterogeneous resources can be efficiently utilized through dynamic resource configuration, the requirements for space-based delay sensitivity and big data application are met, dynamic network connection is adapted, the reliability of service flow is guaranteed, and load balancing of the system is achieved.

The invention discloses a vehicle edge computing network task unloading load balancing system and balancing method, and the system comprises a remote cloud center, the remote cloud center builds connection with each road side unit, a plurality of road side units are uniformly distributed in a road extension direction, and a wireless network connection is built between two road side units; each vehicle is provided with a wireless communication module, and the vehicles are connected through the wireless communication modules; wireless network connection is established between the vehicle and theroad side unit; the SDN controller establishes connection with each road side unit. In a FiWi enhanced vector task unloading system structure based on an SDN controller, the communication path is optimally selected, under the condition that the computing power of the MEC servers is reduced along with the increase of the number of vehicles which select to unload the computing tasks to the MEC servers, relevant parameters are continuously updated, computing resources of each MEC server are averagely utilized in the load balancing task unloading method, and the unloading method is excellent in performance.

The invention provides an energy Internet of Things data acquisition method and a software gateway using a non-blocking input/output model NIO (Non-blocking Input/Output). Pure software such as load balancing and the like and a cloud computing technology are used for solving the problems in the field of energy internet of things under various sensing equipment and communication equipment access scenes; a traditional pure hardware gateway is high in multi-communication protocol compatibility difficulty, difficult to support high-availability requirements of a system in a high-concurrency scene,poor in dynamic expansion capacity and the like. The implementation process of the method comprises the steps that a collector is connected with a communication gateway, a scheduling task is used fordata collection, data processing and data forwarding.

The invention relates to the technical field of map drawing and map synthesis, in particular to a long and narrow map spot partitioning method and device, computer equipment and a storage medium. Themethod comprises the following steps: acquiring image spot data, and dividing a rule grid to decompose the image spot data into a plurality of sub-data sets; Identifying long and narrow spots in the sub-data sets, and respectively calculating the area sum of the long and narrow spots in each sub-data set; And based on the balance of the area sum, finely dividing the regular grid to complete the partitioning of the long and narrow spots. According to the balance of the area sum, the grids are finely divided; The method has the advantages that the method is suitable for parallel computing, the load balance of a computer is facilitated, the computing efficiency can be improved during large-data-volume computing, computing resources are saved, program crash caused by insufficient memory of thecomputer during computing is prevented, and the stability of data computing is guaranteed.

The invention discloses a Docker migration method and system for a cloud data center. Load information values of all physical nodes in a cluster are collected and quantized; when the condition that the load of a certain node reaches a set threshold value or a container is suspended due to damage is monitored, feedback information is generated, a to-be-migrated container is selected on the overloador no-load node by using a hotspot container selection algorithm, affinity matching is performed according to a resource demand vector of the to-be-migrated container and the residual resource quantity of other nodes, and a target node set is selected. Then, in the target node set, an optimal target node is selected according to a probability selection algorithm, and the to-be-migrated containeris migrated to a target node to recover execution. According to the method, the container migration method is designed by taking the minimum migration amount, the shortest migration time and the lowest energy consumption as principles, so that the loss of the cluster caused by the fault of a certain computing node can be effectively reduced, the reliability and the utilization rate of the existingcluster are improved, the load balance is realized, and higher availability is provided for the cluster.

The invention discloses a high speed network data flow load balancing scheduling method based on a field programmable gate array (FPGA). According to the method, parallel computing of the FPGA is given full play. The method comprises the steps that the front end concurrently executes Hash operation and selects an output port in probability according to a jam degree of array groups, wherein the strategy that an flow mapping port is preferential is adopted, and the rear end concurrently sends a data packet out from a corresponding physical layer (PHY) port according to the output port, enables corresponding information of the data packet to be written into the PHY port in a first in first out (FIFO) mode, feeds back a current maximum remaining data size by periods, counts and feeds back a port with the smallest flow quantity in the period, simultaneously regulates the period for next counting according to rate feedback trends, enables the period for next counting to be suitable for burst flow quantity, and achieves instantaneous balance of high speed network data flows under the premise that flow particle sizes are guaranteed.

The invention provides a data dynamic partitioning system based on a node load. The data dynamic partitioning system comprises a load monitoring acquisition module, a prediction module, a data pre-partitioning module, a data migration module and the like, the node load is predicted by adopting a secondary smoothing method, the AHP and entropy index weight method is combined, corresponding partitioning strategies can be obtained according to different data analysis applications, the load balance of the system is dynamically adjusted, and the response speed of the applications is increased. Thesystem aims at an application scene of a Spark and Messql distributed integrated framework, due to heterogeneity of node resources existing in a distributed environment, in order to reduce data transmission consumption between nodes, computing resources of each node are fully utilized, and parallel computing efficiency of application analysis is improved through load balance. Therefore, the invention provides a node load-based data dynamic partitioning mechanism and strategy to improve the system load balance and improve the response speed of the application, and assists related workers in completing decisions.

Representative apparatus, method, and system embodiments are disclosed for configurable computing. A representative system includes an asynchronous packet network; a plurality of configurable circuits arranged in an array, each configurable circuit coupled to the asynchronous packet network and adapted to perform a plurality of computations; and a dispatch interface circuit adapted to partition the plurality of configurable circuits into one or more separate partitions of configurable circuits and to load one or more computation kernels into each partition of configurable circuits. The dispatch interface circuit may load balance across the partitions of configurable circuits by starting threads for execution in the partition having the highest number of available thread identifiers. The dispatch interface may also assert a partition enable signal to merge the one or more separate partitions and assert a stop signal to all configurable circuits of the one or more separate partitions of configurable circuits.