104 results about "Coscheduling" patented technology

In a parallel computing environment comprising a network of SMP nodes each having at least one processor, a parallel-aware co-scheduling method and system for improving the performance and scalability of a dedicated parallel job having synchronizing collective operations. The method and system uses a global co-scheduler and an operating system kernel dispatcher adapted to coordinate interfering system and daemon activities on a node and across nodes to promote intra-node and inter-node overlap of said interfering system and daemon activities as well as intra-node and inter-node overlap of said synchronizing collective operations. In this manner, the impact of random short-lived interruptions, such as timer-decrement processing and periodic daemon activity, on synchronizing collective operations is minimized on large processor-count SPMD bulk-synchronous programming styles.

A cross-domain network resource reservation scheduler configured to schedule a path from at least one end-site includes a management plane device configured to monitor and provide information representing at least one of functionality, performance, faults, and fault recovery associated with a network resource; a control plane device configured to at least one of schedule the network resource, provision local area network quality of service, provision local area network bandwidth, and provision wide area network bandwidth; and a service plane device configured to interface with the control plane device to reserve the network resource based on a reservation request and the information from the management plane device. Corresponding methods and computer-readable medium are also disclosed.

The invention belongs to the technical field of wireless communication network, and discloses an intention-driven cloud access network system and method. The method comprises the steps that a businessapplication layer obtains a mobile network core application and a service complaint for a vertical industry application, and an intention northbound interface completes the transcoding; an intentionenabling layer constructs an intention-based global dynamic management and arrangement control system through a cloud network service plane, and achieves the unified planning of business intention andthe unified scheduling of resources; an infrastructure layer forms a resource pool through the resource virtualization technology, and provides a ubiquitous connection based on three big 5G application scenes. The method achieves the network cooperation, resource sharing, interface opening and management intelligentization based on an intention-driven cloud ubiquitous intelligent access network,constructs a reconfigurable resource management architecture. The invention also discloses the intention-driven cloud access network method, and the method achieves the cooperative scheduling of network resources, and guarantees the end-to-end services.

The invention relates to a power scheduling operation method, in particular to a wind-fire-water co-scheduling method on the basis of a multi-agent system. The method solves the problem that a conventional power scheduling operation method cannot meet the requirement on the large-scale wind power grid-connected operation. The wind-fire-water co-scheduling method on the basis of the multi-agent system is implemented by adopting the following steps of: 1, constructing a wind-fire-water co-scheduling model on the basis of the multi-agent in a scheduling Agent; 2, acquiring predicted data of a load demand in a power system by a load Agent in the power system; 3, sending acquired basic data and real-time data into the wind-fire-water co-scheduling model on the basis of the multi-agent by each thermal power Agent; and 4, issuing and executing a scheduling command by each thermal power Agent and a hydroelectric Agent. The wind-fire-water co-scheduling method is suitable for large-scale wind power grid-connected operation scheduling.

A system is described for controlling co-scheduling of processes in a computer comprising at least one process and a spin daemon. The process, when it is waiting for a flag to change condition, transmits a flag monitor request to the spin daemon and enables itself to be de-scheduled. The spin daemon, after receiving a flag monitor request, monitors the flag and, after the flag changes condition, enables the process to be re-scheduled for execution by the computer. Since the spin daemon can monitor flags for a number of processes, the ones of the processes that are waiting will not need to be executed, allowing other processes that are not just waiting to be processed more often.

The invention discloses a distributed-type video cooperation scheduling method based on a wireless sensor network, and collaborated cooperation of a plurality of video nodes is adopted. Scheduling is conducted in a dynamic and self-adaptive mode, and an appropriate video sensor node is selected to carry out present video transmission. In a state without invasion, a system works in a static state scheduling state, and the nodes enter semi-activation states by turns according to the time assigned by a system scheduling server. When an invasion state occurs, and an invasion area is determined firstly according to dynamic group scheduling, and nodes in other areas maintain in dormancy states; and redundancy nodes are made to enter the dormancy states according to a CPNS algorithm. Acquisition transmission nodes are in activation states, and other nodes in the area enter the half-activation states. According to the method, a centralized type scheduling method and the distributed type scheduling method are combined, and through the provided method, problems in the aspects such as node energy consumption, energy equalization and transmission management of video acquisition transmission nodes are preferably solved, and the rationality and the energy equalization of video transmission in the wireless sensor network are raised.

The invention relates to a production and transportation cooperative scheduling method and a production and transportation cooperative scheduling system based on improved harmony search. The production and transportation cooperative dispatching method comprises the steps of: 1, performing group batching on work pieces; 2, setting algorithm parameters; 3, generating an initial harmony library; 4, calculating an adaption degree value; 5, acquiring a negative ideal solution of the harmony library; 6, generating a new harmony; 7, performing harmony library searching operation on the new harmony, and updating the new harmony; 8, comparing the new harmony with the negative ideal solution, if the new harmony is superior to the negative ideal solution, substituting the negative ideal solution and updating the harmony library; 9, updating a global optimal solution; 10, and judging whether a termination condition is satisfied, if so, outputting the global optimal solution, otherwise, returning to the step 4. The production and transportation cooperative scheduling method and the production and transportation cooperative scheduling system can realize the optimization of overall efficiency of the supply chain, thereby reducing cost of the supply chain, and enhancing customer satisfaction level.

The invention discloses a coordinated scheduling method with AA-CAES for grid power and standby capacity. The method comprises a step of establishing an objective function of a grid power and standbycapacity coordinated scheduling model including power purchase cost, purchase standby cost and wind curtailment cost, a step of establishing constraints of the grid power and standby capacity coordinated scheduling model including AA-CAES power plant operation constraints, AA-CAES power plant standby capacity constraints, system constraints and conventional unit constraints, wherein positive and negative standby capacity constraints in the system constraints are expressed by using a fuzzy opportunistic constraint form, a step of carrying out clear equivalence processing on wind power unit output error and load prediction error fuzzy numbers of system positive and negative standby capacity constraints in the conventional unit constraints, carrying out linear processing on the AA-CAES powerplant operation constraints, and obtaining a clear equivalent model of grid power and standby capacity coordinated scheduling, and a step of solving the clear equivalent model by using optimization software to obtain an optimal power and standby capacity purchase plan of a grid operator.

The invention discloses an electric hot water multi-energy-flow cooperative scheduling method based on convex optimization. An electric hot water multi-energy-flow cooperative scheduling non-convex programming model is established; a convex relaxation and approximate processing method is adopted to convert a non-convex programming model into a second-order cone programming model, a penalty function method is adopted to construct a strengthening model to reduce relaxation errors, and finally a scheduling result is obtained through iterative solution of the model, so that collaborative optimization of the electric hot water heterogeneous energy flow is realized, and the safety and economy of system operation are improved.

The invention belongs to the technical field of edge computing, particularly relates to an edge computing task cooperative scheduling method, system and device, and aims to solve the problems that anant colony algorithm is prone to local optimization and cannot well solve the problems of low resource utilization rate and unbalanced virtual machine computing resource load in an edge environment. The method comprises the steps of obtaining a scheduling queue of an edge computing task, and initializing related parameters; starting from the first ant in the ant colony algorithm, obtaining a feasible solution of the current ant; if the solution is the optimal feasible solution, updating the optimal solution and the shortest task maximum completion time; updating local pheromones until all antscomplete solution searching; carrying out global pheromone updating, and carrying out local optimization in an iterative mode until the set number of iterations is reached, and obtaining a globally optimal feasible solution and the corresponding shortest task maximum completion time are obtained. According to the method, the ant colony algorithm is further prevented from falling into local optimum too early, and load balance, reasonable task scheduling and efficient resource utilization are guaranteed.

The invention discloses an industrial field big data task coordination degree method based on edge calculation. The method comprises the steps: S1, enabling a scheduler to obtain a task amount Ni and a time delay Ti; S2, evaluating the priority of each task; S3, obtaining the network residual bandwidth R and the uploading rate Li of the tasks, wherein Li represents the uploading rate of the ith task, task queuing waiting is carried out according to the priority, and delay Wi is recorded; S4, obtaining a task scheduling threshold alpha of the scheduler, calculation information of each edge server, the number l of task queuing processing and queuing waiting delay; and S5, carrying out cooperative scheduling on the tasks according to a system resource state, a current business task amount and a delay requirement. According to the invention, resource scheduling of the task between the terminal and the edge server is realized through edge computing, reasonable scheduling of the industrial field task between the terminal equipment and the edge server is achieved, computing resources of the system are fully considered, the task processing capability of the system is improved, the resource consumption of the system can be minimized, and the real-time performance of task scheduling is improved.

The invention relates to a river and lake water system comprehensive cooperative scheduling system and a scheduling method thereof, which adopt a mode of combined solution of a drainage basin water quantity and quality mathematical model and a water resource multi-target cooperative combined scheduling model to perform decision optimization of a drainage basin combined scheduling scheme. The TaihuLake basin water resource joint scheduling decision-making system capable of guaranteeing water safety is formed by constructing a basin water resource multi-target cooperative joint scheduling model, expanding and perfecting scheduling target management, scheduling scheme analysis and other functions on the basis of an existing basin river network water quantity and quality mathematical model, developing an optimization decision-making module and a corresponding interface and coupling the optimization decision-making module and the corresponding interface. A scheduling scheme set satisfyingmultiple goals of a drainage basin and a region is proposed; different scheduling scheme sets are simulated by a drainage basin water quantity and water quality mathematical model, a water quantity and water quality simulation result is used as input of a water resource joint scheduling decision, and optimal decision of a drainage basin water resource multi-target cooperative joint scheduling scheme is realized through calculation and evaluation of a joint scheduling decision model.

The invention provides a joint scheduling method based on urban public transport resources, which focuses on a bus system and a shared bicycle system, uses a reinforcement learning long-term optimal scheduling and cooperative scheduling strategy on the basis of time-space prediction, can achieve cooperative scheduling between the bus system and the shared bicycle system, solves the possible localgreedy problem, can schedule other traffic resources can be dispatched in time to meet travel requirements of users when a certain traffic service is temporarily unavailable or inapplicable. The method comprises the following steps: according to recorded crowd flow data at different time and places and people flow changes borne by various vehicles, pre-constructing a time-varying demand flow graphfor people to take the vehicles; and then, taking the current station state and the future predicted flow diagram as the state of the current system, and performing collaborative and efficient scheduling on the current multiple traffic systems by utilizing a reinforcement learning technology.

The invention discloses a source network co-scheduling method including a controllable series connection compensation device. The method comprises the following steps: step 1, relevant calculation parameters are given; step 2, the operation mode of a controllable series connection compensation device is modeled; step 3, an optimized model is constructed, the optimized model is based on the target that the sum of the power generating cost of a power system and the cost of abandoned wind power is minimal, and the optimized model includes a number of constraints; and step 4, the optimized model is simplified and employs a mixed-integer quadratic programming method to solve the problem, and a final source network co-scheduling scheme is obtained. The method integrates the controllability of current flow control equipment and energy storage systems and the like into economic scheduling decision-making of a power grid, and constructs the source network co-scheduling model of the wind and electricity coexistence in the power grid to determine the economic operation mode under the restriction of the security constraint level, can enhance the consumption of fluctuation of wind power, load, and so on injected into nodes under the premise of ensuring the safe operation of the power grid, and improves the economic benefit of power grid operation.

The invention provides a production and maintenance scheduling method and system based on an improved grey wolf algorithm, and relates to the technical field of production and maintenance scheduling. The method comprises the following steps: randomly generating N one-dimensional vectors for a workpiece in the production and maintenance process to form an initial solution of the grey wolf algorithm, then improving an iteration parameter and an iteration mode in the original grey wolf algorithm, adding a probability-based neighborhood search mechanism, and optimizing the original grey wolf algorithm to obtain a grey wolf algorithm solution; and finally, inputting the initial solution into an improved grey wolf algorithm to solve a globally optimal solution, and carrying out cooperative scheduling on production and maintenance by utilizing a workpiece processing sequence and a maintenance opportunity scheme corresponding to the globally optimal solution. The searching capability of the wolf pack is enhanced, the updating efficiency of the wolf pack is ensured, the diversity of populations is improved, the convergence capability of the algorithm in the last stage is enhanced to a certain extent, and the problem that in the prior art, cooperative scheduling cannot be carried out on production and maintenance when multiple influence factors are considered at the same time is solved.

The invention relates to a subtask cooperative scheduling method for a mobile edge computing system, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, modeling a mobile edge computing system; S2, modeling a mobile device variable; S3, modeling an edge server variable; S4, modeling a sub-task model; S5, modeling sub-task completion time; S6,modeling local calculation mode subtask completion time; S7, modeling the completion time of an unloading mode subtask of an edge server; S8, modeling a subtask unloading and scheduling constraint condition; and S9, determining a sub-task unloading and cooperative scheduling strategy to minimize the maximum completion time of the task. According to the method, a causal dependence relationship among sub-tasks, a time delay requirement of a to-be-executed task and the task processing performance of the edge server are comprehensively considered, and the task unloading and scheduling strategy iscombined, so that the maximum completion time of the system is minimized.

The invention relates to a distributed cooperative scheduling method for a multi-micro-energy-network coupling system, and the method comprises the following steps: S1, building a multi-micro-energy-network coupling system optimization scheduling model, and carrying out the linear processing of the multi-micro-energy-network coupling system optimization scheduling model; s2, decoupling the multi-micro-energy network coupling system optimization scheduling model into a multi-sub-micro-energy network optimization scheduling problem; s3, solving the distributed optimal scheduling model of the multi-micro-energy network coupling system by adopting an alternating direction multiplier method; s4, initializing alternating direction multiplication parameters and setting the number of iterations; s5, enabling each micro-energy network to adopt a non-convex alternating direction multiplier method to solve the respective optimization scheduling problem, and obtaining a decision variable value; calculating original and dual residual errors; and S6, judging whether a convergence condition is met or not, if so, stopping iteration, and outputting an optimal scheduling problem result, namely an optimal scheduling scheme, otherwise, updating the dual variable and the number of iterations, returning to the step S5, and continuing iteration. The method and the system are beneficial to providing an optimal scheduling scheme meeting the load requirement.

The invention discloses an electric system network cluster task allocation method based on load balancing. The method comprises the following steps that: according to computing and communication properties of each processor of the system and requirement of a requested task, an information system defines a time table of the task; then the system performs static allocation according to the time table based on the task; and finally the system performs co-scheduling and process migration according to an actual operating situation, so as to relieve the choke point pressure and improve the efficiency of the system. The method provided by the invention is an electric scheduling method based on a load balancing allocation algorithm, which is suitable for large-scale electric network.

The invention discloses an automatic allocation method and a system for multi-product shared resource and relates to the field of service activating of telecommunication operators. The method comprises the steps that the resource is confirmed according to multiple products, a shared access mode and a terminal are selected and transmitted along an order of a customer when a customer is accepted by a reception desk; a multi-product resource allocation work order is dispatched to a resource management system during a resource allocation link of a service activating course; the resource management system generates a shared resource and individualized resource allocation job order according to a bearing relationship between the resource and the products; a resource scheduling engine of the resource management system conducts serial-parallel resource allocation and scheduling on the shared resource and individualized resources; and the resource management system feeds a multi-product resource allocation result back to a server for activation. The complicated product resource sharing relationship, resource allocation sequential control and synergic scheduling are shielded in the resource management system, the essences of a resource allocation flow and the bearing relationship between the regression products and the resource are not required to be described in advance, and the flow quantity is reduced greatly.

The invention provides a power distribution network source network load storage collaborative optimization scheduling model and method considering carbon emission, which are used for collaborative scheduling of a load part and an energy storage part of a power distribution network. The method comprises the following steps: S1, selecting a load storage element of a load storage collaborative scheduling model by taking an optimized carbon emission level as a standard; s2, taking the carbon emission level of the power distribution network, the scheduling operation cost and the voltage stability as optimization objectives, and taking the active output of the distributed power supply, the reactive output of the distributed power supply, the input level of a switchable capacitor, the tap adjustment of an on-load tap-changing transformer and the network frame of the power distribution network as decision variables, establishing a power distribution network source network load storage collaborative optimization scheduling model considering carbon emission; s3, solving the scheduling model established in the step S2 by using a multi-target particle swarm algorithm with elite strategy rapid non-dominated sorting; and carrying out scheduling according to a solving result; The method can effectively reduce the carbon emission level of the power distribution network, improves the operation economy of the power distribution network, and improves the voltage stability of the system.

The invention belongs to the technical field of high-performance computers and provides a collaborative scheduling method and system based on a GPGPU system structure. The collaborative scheduling method comprises the steps that two priority scheduling queues are combined into one priority scheduling queue in an Issue stage, and the priority scheduling queue formed after combination is used as a priority scheduling queue in a Fetch stage; in the Fetch stage, instructions are obtained from the priority scheduling queue formed after combination; the obtained instructions are decoded; in the Issue stage, parallel execution of the decoded instructions is conducted through two schedulers in the Issue stage, and the decoded instructions are transmitted according to the respective scheduling strategies of the schedulers; after the transmitted instructions enter an assembly line, execution is conducted; an execution result of the instructions are written to a specific position. By the adoption of the collaborative scheduling method and system based on the GPGPU system structure, the GPGPU performance can be effectively improved.

The invention discloses a method and a system for dynamically scheduling a network operating system, solving the problem that in the prior art, a passive defense technology based on feature matching cannot resist unknown threats. The method comprises the steps as follows: a system is initialized at first; a sensor is responsible for assessing working conditions of all NOS, and giving an assessment report; the sensor periodically sends the assessment report to a scheduler, and updates registration information; and if the NOS assessment result of a current Master role is normal, a scheduling timer is determined to be zero, and otherwise, the scheduler stops timing. According to the method and the system provided by the invention, traditional cooperative scheduling of network resources, computing resources and storing resources can be achieved, security of the network operating system is also greatly improved, and the passive situation that at present the existing network operating system defends passively and only has acquired immunity is solved.

The invention provides a task and data collaborative scheduling method for a wide-area high-performance computing environment. The method comprises the following steps: firstly, constructing a task completion time model according to conditions such as a global resource state, a computing task demand and a data layout condition; secondly, according to an optimal scheme selection mechanism, selecting an optimal center-level cooperative scheduling scheme based on a task completion time estimated value and a global resource use state; and then, realizing data redundancy layout based on the data access frequency in the scheduling process. And finally, generating a queue-level scheduling scheme based on a task stealing mechanism so as to correct estimated value deviation of task completion time.According to the method, the computing tasks and the corresponding data can be cooperatively scheduled among a plurality of cross-domain centers and task queues, task allocation and data layout are efficiently and reasonably carried out, and the system time performance and the global resource utilization rate are both considered, so that efficient computing is realized.

The invention discloses a neighborhood information-based cooperative scheduling method for scattered charging piles. Charging pile objects change charging status flags at the beginning of a t time period according to a plan made in the previous time period; and charging piles acquire power generation information and power consumption information of current other objects within a certain radius of a neighborhood, calculate charging probability thresholds in a t+1 time period by combining the condition whether a cooperative scheduling flag is received or not, compare the charging probability thresholds with a random number generated at random and make a decision whether charging is carried out or not. According to the neighborhood information-based cooperative scheduling method, layering is not needed, coordination between an upper layer and a lower layer is not needed and the algorithm structure is simple. The scheduling method is suitable for the characteristics that the charging piles are wide in distribution and small in capacity; the investment in a scheduling system can be reduced; and more flexible configuration is achieved.

The invention provides a container cloud-oriented task collaborative scheduling system and method thereof. An environment feature extraction module is used for extracting feature indexes required by task collaborative scheduling from a current resource environment, and supporting a scheduling scheme construction module to carry out the judgment of the node scheduling capability; the scheduling scheme construction module is used for constructing an optimal scheduling scheme in the current environment for the current to-be-scheduled task; the scheduling scheme execution module is used for executing a scheduling scheme, namely, scheduling a task to be scheduled to an optimal scheduling node specified by the scheduling scheme; and the scheduling evaluation and feedback adjustment module is used for evaluating the actual scheduling effect of the optimal scheduling scheme, that is, the improvement condition of the environment resource state after task scheduling is judged according to an evaluation rule, and the judgment result is fed back to the scheduling scheme construction module for improving the scoring accuracy of the scoring algorithm. According to the invention, the resource convergence effect and the scheduling performance are improved in the aspect of multi-target task scheduling with different dependency feature relationships.

The invention provides a micro-grid group cooperative scheduling method and device, electronic equipment and a storage medium, and belongs to the technical field of micro-grid group cooperative scheduling control. Each micro-grid in the micro-grid group comprises a diesel generating set, a wind generating set, an energy storage device and an electrical load, and an electric vehicle cluster is used as mobile energy storage between the micro-grids. The method comprises the following steps: constructing a micro-grid group collaborative scheduling model; converting the micro-grid group collaborative scheduling model into a corresponding Markov decision process model; optimizing the Markov decision process model by using an SAC algorithm to obtain an optimal strategy model; and outputting a real-time scheduling instruction of the micro-grid group according to the optimal strategy model. According to the invention, collaborative scheduling of the micro-grid group containing the electric vehicle cluster can be realized, and the method has the characteristics of wide compatibility and high adaptability.

The invention provides a cooperative scheduling method for distributed manufacturing and multi-mode transportation of high-end equipment, and relates to the technical field of task scheduling. According to the invention, distributed manufacturing, learning effects, multiple transportation modes and production and transportation collaboration are combined together, and learning effects of factories distributed at different positions in the production process and multiple transportation modes of the transportation process are considered in the classical production and transportation collaboration problem, so the research content is closer to the actual production environment, and the adaptability of the obtained scheduling scheme is improved.

The invention discloses a multi-energy collaborative scheduling processing method and a multi-energy collaborative scheduling processing device. The method comprises the steps that a scheduling strategy of an active power distribution network is acquired, the power distribution network is divided into three levels of a power distribution network, a feeder line and an autonomous region by the scheduling strategy, and the scheduling strategy is called to perform scheduling according to different levels; the autonomous region comprises a distributed power supply, an energy storage unit and a flexible load; and the active power distribution network is scheduled according to the scheduling strategies under different time scales. According to the method, the technical problems in the aspect of cooperative control of the multi-energy system with multiple time scales are solved.

The invention provides a man-machine cooperative scheduling method and device, electronic equipment and a storage medium. The method comprises the steps: receiving target task information; selecting atask executor identifier from a robot identifier and a worker identifier according to the target task information and a configured scheduling rule; and sending the target task information to a terminal corresponding to the task executor identifier. According to the method, the cooperative scheduling of the robot and the staff is achieved, and then the task execution efficiency is improved.