310 results about "Resource allocation algorithm" patented technology

The present invention relates to a resource allocation apparatus and method. The resource allocation apparatus includes a job information management unit for managing job characteristic information required to execute jobs input by a user. A resource form selection unit selects an initial resource allocation form required to execute each job, based on the job characteristic information. A resource allocation unit allocates resources required to execute the job based on the initial resource allocation form.

The invention provides a resource allocation method, a resource allocation system, a terminal and a base station for direct communication of the terminal. The resource allocation method applicable to the terminal and used for direct communication of the terminal comprises the steps: detecting a time-frequency resource already occupied by other D2D terminals when a data transmission service is needed; selecting the time-frequency resource for processing the data transmission service according to the time-frequency resource occupied by other D2D terminals and the attribute information of the data transmission service. According to the technical scheme, the problem of the confliction in the allocation of the time-frequency resource can be avoided, the resource selection problem when the time-frequency resource is insufficient can be effectively solved, and reasonability in allocation and application of the resource can be realized.

The invention is applicable to the technical field of computers and provides a resource allocation method and a resource management platform. The method comprises the following steps of receiving a resource request sent by a resource requester, wherein the resource request contains resource demand and resource application characteristic; determining a host machine allocating resources to the resource requester according to the resource request and a resource application characteristic allocation strategy; and controlling the host machine to allocate resources to the resource requester, and returning resource allocation information to the resource requester, thereby solving the problems in the prior art that hardware resource competition occurs and the overall performance of the host machine is reduced as a plurality of application programs share hardware resources, effectively improving the utilization efficiency of server resources, reducing the hardware loss, and improving the user experience.

The invention discloses a D2D resource allocation method based on multi-agent deep reinforcement learning, and belongs to the field of wireless communication. The method comprises the following steps:firstly, constructing a heterogeneous network model of a cellular network and D2D communication shared spectrum; establishing a signal to interference plus noise ratio (SINR) of a D2D receiving userand an SINR of a cellular user based on the existing interference, respectively calculating unit bandwidth communication rates of a cellular link and a D2D link, and constructing a D2D resource allocation optimization model in a heterogeneous network by taking the maximum system capacity as an optimization target; For the time slot t, constructing a deep reinforcement learning model of each D2D communication pair on the basis of the D2D resource allocation optimization model; And respectively extracting respective state feature vectors from each D2D communication pair in the subsequent time slot, and inputting the state feature vectors into the trained deep reinforcement learning model to obtain a resource allocation scheme of each D2D communication pair. According to the invention, spectrum allocation and transmission power are optimized, the system capacity is maximized, and a low-complexity resource allocation algorithm is provided.

The invention discloses a resource allocation method, a resource allocation device and a resource allocation system. The resource allocation method includes the steps: utilizing a virtual machine manager to receive virtualized business characteristic information on a first server of current handling business reported respectively by all virtual machines; and allocating physical resources to the virtual machines according to the business characteristic information. By the aid of the resource allocation method adopting the scheme in the embodiment, resource allocation for the virtual machines is more reasonable, and service quality of the virtual machines for business handling is improved.

The invention discloses a mode selection and resource allocation method for device-to-device (D2D) users in a cellular system, which reduces the algorithm complexity of the mode selection and resource allocation for the D2D users, and the obtained mode selection and resource allocation for the D2D users can reduce the transmission power needed by the D2D users. The method comprises two steps of mode and resource demand calculation and analysis and relevant algorithm for the resource demand and supply relation. The mode and resource demand calculation step comprises the following steps of calculating the minimal transmission power needed by each pair of D2D users under a cellular mode, a special channel mode and a multiplying channel mode; calculating the power difference of the D2D users under the three different modes; calculating the mode demand and the resource demand of each pair of D2D users; and counting the sum of the resource demand of the system. The analysis and relevant algorithm step for the resource demand and supply relation comprises the steps of further obtaining a mode selection and resource allocation algorithm under each situation according to the resource demand and supply relation of the system.

The invention belongs to the field of cooperative transmission technology, and discloses an energy acquisition cooperative network resource distribution method based on simultaneous transmission of information and energy. The method comprises the steps of acquiring channel state information on each sub-carrier in a transmission time slot; according to a resource distribution algorithm, calculating optimal power distribution on each sub-carrier and determining sub-carrier distribution, increment strategy and optimal power splitting ratio; in a first time slot in a useful data transmission process, broadcasting data in each sub-carrier according to a calculated power, receiving by a relay and a target end; wherein a relay forwarding strategy is utilized, one part of the source transmitting power is used for information transmission of the time slot, and the other part is acquired by the relay for information transmission of a next time slot; and the increment strategy determines a transmission strategy of the second time slot, namely the relay forwards the information to a target end according to the calculated power or transmits new data to the target end according to the power which is obtained through calculation. The energy acquisition cooperative network resource distribution method improves system capacity and spectrum utilization rate on the condition that a requirement for lowest rate of each user is satisfied.

The invention relates to an automatic resource distribution method in a cloud calculation environment. The method comprises the following steps: A. a "resource pool" in the cloud calculation environment is defined and managed; B. a virtual machine is pre-established; C. when a resource application is put forward by a user, resources in the resource pool is automatically matched by a management system, and the resources are listed to an optional queue if types and configuration are both matched; if the number of the optional queue surpasses a requirement of the user, the resources are selected from the optional queue to be distributed to the user according to a presorting dynamic resource distribution algorithm; D. when the pre-established virtual machine cannot meet the requirement of the user, insufficient number of the virtual machines are established after the resource application of the user is submitted. E. the resources are automatically applied and returned, and user data are confidentially stored or selectively deleted according to the requirement; and F. statics and analysis are performed on the requirement of the user, and a resource distribution report form is automatically generated. The process of the method is rapid and safe and automation is realized so that complexity and management cost of resource distribution in the cloud calculation environment are reduced, working efficiency is enhanced and resource utilization rate is also scientifically enhanced.

The invention relates to a joint radio resource management method based on a cross-layer and used to a self-adapting MIMO-OFDM system, comprising: when a plurality of users initiate access requirement, a classifier processes to classify according to service types of the access requirement, and CAC module decides if the access requirement is allowed to accept according to different QoS requirements of a heterogeneous service and resource conditions; a priority scheduling mechanism faces the users, and a scheduler processes to schedule for the heterogeneous service types, different QoS requirements, and user preference information according to priority rules, and transmits information to a physical layer module, so as to determine the users of each time slot which is provided with resource allocation options; the physical layer adopts a resource allocation algorithm to allocate sub-carriers, loading bit streams and allocate corresponding transmission power according to data link layer information, and designs a net utility function for each classification on the base of the game theory, and transforms a target of pursuing net utility function into solving a non-cooperation game process. Compared with traditional methods, the method shows better performance advantages on outage probability, degree of satisfaction of the users for the service and system throughput.

The invention relates to a resource allocation method for communication system comprising: predicating the status information of resource of next time, acquiring predication information about resource status of next time; performing resource allocation according to predication information. The invention also relates to a resource allocation system in communication system comprising a status predication module, for predicating status information for resource of next time, acquiring predication information for resource status of next time; a resource allocation module, for allocating resource of next time according to the predication information. The invention adopts resource allocation scheme based on predication information, overcomes time hysteresis for allocating system resource based on present resource status, further optimizes resource allocation course and largely improves user performance.

A resource allocation method, a resource allocation program, and a resource allocation apparatus in which a request reception server subjects an inputted SQL to a syntax analysis. At least one SQL process is extracted from the input SQL, and a resource cost of a database required by a BES (Back End Server) to perform the SQL process for each of one or more process types contained in the SQL process is calculated. Further, an allocation ratio is determined for allocating the resource of a request executing server to a virtualized server in accordance with a resource cost ratio required by each of the BES to execute the SQL process. Additionally, requests are made for execution of the respective BES on the virtualized server to which the resource has been allocated so as to execute the SQL process.

The invention provides a resource allocation method and device. The resource allocation method comprises the steps that a certain number of service instances are created in a resource pool of a cloud computing platform in advance, wherein each service instance contains multiple resources; a resource application request of a user is received; the service instance satisfying the resource application request of the user is found from the service instances created in advance, and the resources in the service instance are allocated to the user. Due to the fact that multiple created service instances are prestored in the resource pool and each service instance contains multiple resources, quick response can be conducted when the resource application request of the user is received, and the resources in the corresponding service instance are allocated to the user; as a result, the waiting time of the user is saved, and user experience is improved.

The invention discloses a resource allocation method in a cloud computing system. The method comprises the following steps of: S1, obtaining the computing node parameters of computing nodes in the cloud computing system; S2, obtaining a resource need parameter according to an access request of a user; and S3, selecting the computing nodes, of which the computing node parameters meet the resource need parameter, from the cloud computing system, and allocating the resource of the selected computing nodes to the user for carrying out the access request. With the resource allocation method in the cloud computing system described by the embodiment, the resource can be allocated according to the user request in the cloud computing system, under the condition that virtual servers change frequently in the computing system, a dynamic load balancing strategy can be realized, the efficiency is improved, and the effective allocation of resource is guaranteed.

The invention discloses an intelligent computing unloading method under a vehicle-mounted network, which combines a deep reinforcement learning algorithm with vehicle edge computing, studies the optimization problem of task scheduling and resource allocation in the vehicle-mounted network, and gives a computing unloading method. On the basis of the limited Markov chain, the intelligent unloading model under the vehicle-mounted network is constructed, and the service experience quality of a user is considered while the profit of a network operator is maximized. According to the invention, the joint optimization problem of initial task scheduling and resource allocation is decomposed into two sub-optimization problems. For the first sub-problem, a utility function is designed to measure theservice experience quality grade of a user, and then a task scheduling algorithm based on a bilateral matching model is provided to solve the sub-problem. Based on an improved deep reinforcement learning algorithm, a reinforcement learning model under a vehicle-mounted network unloading system is constructed, and a resource allocation algorithm is provided to solve a second sub-problem. Experimental results prove the high efficiency of the algorithm provided by the invention.

The invention relates to a chaotic particle swarm optimization-based orthogonal frequency division multiplexing (OFDM) system resource allocation algorithm and belongs to the technical field of system resource allocation. The invention provides a utility function maximized framework-based resource allocation algorithm aiming at the power allocation problem in a wireless multi-user orthogonal frequency division multiplexing (OFDM) system. In an actual network environment, the optimized algorithm is non-convex and a classical optimization method is difficult to solve the problem. Therefore, a particle swarm method in intelligent optimization is applied to non-convex optimized algorithm design. Logistics chaotic searching is embedded into a PSO algorithm aiming at the problem that particle swarm optimization easily has a local extremum point, and the chaotic particle swarm algorithm is provided. Compared with like algorithms, the novel algorithm effectively solves the non-convex problem, can make the system have better performance, and can effectively prevent the particle swarm algorithm from having a local solution.

The invention provides an energy efficient resource distribution algorithm based on interference coordination in an ultra-dense wireless network. A clustering-based ultra-dense wireless network is analyzed and given; inter-cluster and in-cluster interference coordination strategies are provided; inter-cluster interference coordination is carried out by adopting a graph colouring theory; and in-cluster interference coordination is carried out in a partial information interaction manner. The energy efficient resource distribution algorithm based on interference coordination in the ultra-dense wireless network is provided. Joint optimization of sub-channels and power of the ultra-dense wireless network is carried out through the algorithm. Firstly, the sub-channels are distributed by utilization of a user quality experience function and maximum and minimum algorithms; and secondly, optimization distribution of the power is carried out by optimizing an improved particle swarm algorithm, wherein to prevent particle swarm optimization from getting into local solution defect, a particle swarm optimization algorithm is improved by introducing damped vibration and fitness variation.

The invention discloses a Kubernetes dispatching optimization method based on a neural network. The method comprises building of a prediction model and a resource allocation algorithm; the memory consumption of a Node in Kubernetes is predicted through the recurrent neural network; the change of the memory consumption in a future period of time is predicted; memory consumption data is input into the resource allocation algorithm to calculate out the number of instances needed to be added; and a Kubernetes system performs dynamic expansion according to the obtained number of the instances, so that a memory resource-based dynamic expanding and contracting function of the Kubernetes is completed. According to the method, the problem that the stability of the system is reduced due to excessively high memory resource consumption in container application of the Kubernetes can be solved.

The invention relates to a resource allocation method oriented to green cloud computing. A virtualization technology is adopted to abstract a task scheduling problem into a virtual machine deployment problem, prediction is carried out on task load requested by a user, conservative control strategies are adopted by combining current system states and resource distribution to allocate and control system resources ahead of time, energy consumption of a cloud computing system is lowered, and waste of resources is avoided.

The embodiment of the invention discloses a computing resource allocation method, a cloud management platform node and a computing resource cluster. By the invention, the construction cost of service can be reduced, and the equipment utilization rate of computing resources in the execution process of the service is improved. The method comprises the following steps that: the cloud management platform node receives a service busy / idle state which is sent by a virtual machine or the attribute information of the virtual machine, wherein the virtual machine operates a telescopic service module; and the cloud management platform node allocates the computing resources to the service according to the attribute information or the service busy / idle state. The invention is applicable to the field of computer application.

The invention requests for protecting a resource distribution method based on full-duplex D2D communication in a cellular network. Full-duplex (FD) D2D (Device-to-device) communication is introduced in the cellular network; therefore, the burden of a base station can be reduced; the energy consumption of a terminal can be reduced; the cell coverage range can be enlarged; but, resource sharing of D2D users and cellular users brings about a certain same-frequency interference to a system; mutual interference can be reduced through reasonable resource distribution; a resource distribution algorithm having the maximum weight matching is provided in the invention; on the premise that the service quality requirements of the cellular users are ensured through the algorithm, the lowest requirements of D2D link communication can also be ensured through the algorithm; the resource distribution relationship of the cellular users and the D2D users in the system is equivalent to the vertex matching relationship in a graph theory; wireless resources are reasonably distributed between D2D user pairs and the cellular users by utilizing the maximum weight matching algorithm in the graph theory; interference between the cellular users and the D2D users can be effectively reduced; the relatively good fairness can be obtained; and simultaneously, the throughput capacity of the system is maximized.

The invention relates to a resource allocation scheme in the field of wireless communication, and specifically relates to a resource allocation scheme which can ensure the joint optimization of power and carriers of user experience quality of a terminal under a multi-user and multi-service scene in an orthogonal frequency division multiplexing (OFDM) system, which belongs to the technical field of communication. According to the resource allocation scheme, a gradual resource allocation algorithm (GRAA) is adopted for calculating sub-carrier and power allocation and allocating appropriate number of carriers and power for each user according to different sensitivities of demands of various services on resources and different channel states in which the users are, thereby ensuring QoE (quality of experience) of each user and simultaneously saving resources to the greatest extent.

The invention relates to a task scheduling and resource allocation method and system for a real-time cloud platform. According to the task scheduling and resource allocation method and system for the real-time cloud platform, a global state storage module obtains the running state of the cloud platform and reports the running state to a global state monitoring module; the global state monitoring module develops a corresponding scheduling strategy through a task allocation matrix, a task adjacency matrix and a mask matrix on the basis of the running state; node-driven and / or task-driven task scheduling and resource allocation are performed on the real-time cloud platform on the basis of the scheduling strategy. The task scheduling and resource allocation method and system for the real-time cloud platform can take the relationship among tasks into full consideration during task allocating and reduce traffic among nodes and bandwidth pressure so as to improve the performance of the platform, can perfectly adapt to all situations of dynamic scheduling of the cloud platform to ensure that the cloud platform can maintain high computing performance and resource utilization rate at all times during operation, and are low in time complexity and applicable to cloud environments with large-scale nodes and large amount of tasks.

The invention discloses a microcloud computing resource allocation method based on a pricing mechanism in an edge computing environment. The concept of a central controller is introduced, idle resources of cooperation microcloud are coordinated and utilized, the goal of reducing transmission delay and energy consumption is realized, the relationship between the microcloud and the central controller is modeled as a double-phase Stackelberg game, each cooperation microcloud determines the computing resource quantity required in the deployment phase, and the central controller coordinates the resource sharing between microcloud in the operation phase, so that the resource utilization rate of an edge computing system based on microcloud is increased, and the cost of a network service provideris reduced to the utmost extent when the user service quality is maintained.

The invention belongs to the technical field of wireless communication. The invention discloses a collaborative computing unloading and resource allocation method and application. According to the method, the weighted sum of computing rate of mobile edge computing and the throughput of a block chain system is maximized by jointly optimizing collaborative unloading decisions, power allocation and the block size and the block time of the block chain system; optimal compromise of the performance of two subsystems is realized. Specifically, firstly, a computing task of a mobile device is unloadedto an MEC server to be executed through cooperative communication; secondly, a computing unloading transaction from an MEC system is processed by adopting a block chain technology; in order to ensurethe security of data in an integrated system, a trust computing model is considered in cooperative unloading and block generation. According to the method, a joint optimization problem is converted into a Markov decision process problem, and an efficient unloading decision and resource allocation algorithm based on deep reinforcement learning is put forward.

The embodiment of the invention discloses a resource allocation method, network side equipment and a terminal. The method comprises the following steps: enabling the network side equipment to allocate uplink resources of a low-latency service through a configuration or reservation mode; and generating uplink scheduling information based on the allocated uplink resources, and sending the uplink scheduling information.

The invention provides an end-to-end network slice resource allocation algorithm based on deep reinforcement learning (DQN) for the situation that multiple slices of a hybrid service share wireless resources, which algorithm dynamically and reasonably allocates the resources by jointly considering the influence of an access side and a core side from an end-to-end perspective. In order to obtain areasonable DQN network through training, feedback of an environment in the DQN is solved, an end-to-end system access rate optimization problem is decoupled into an access side part and a core side part, and then a dynamic knapsack algorithm and a maximum access link mapping algorithm are designed respectively to obtain maximum end-to-end access. According to the method, resources can be dynamically adjusted by utilizing the trained network no matter in a static environment or a dynamic environment, so that the system access rate is remarkably improved.

The invention discloses a container-based industrial Internet of Things edge computing resource allocation method and system, and the method comprises the steps: dividing tasks into n types accordingto data types collected by a sensing layer, and obtaining a probability relationship that the tasks arrive at an edge server cluster in sequence through historical data statistics; allocating computing resources to the real-time tasks according to the system state space and the decision space, and performing sequence decision; according to a resource allocation algorithm based on reinforcement learning, the system selects the most reasonable strategy from the low resource allocation strategy, the middle resource allocation strategy and the high resource allocation strategy to allocate resources to the current task according to the current state; and constructing a task scheduling processing model, creating a container, scheduling the data to the container for processing and analysis, and deleting the container after the task is completed to complete distribution. Under the condition that resources are limited, different types of data computing resources are distributed, the container is created for processing and analysis, and the resource use efficiency and the total task processing rate are improved. The method is suitable for the field of industrial Internet of Things data processing.

The invention discloses a cloud computing resource distributing method based on energy consumption optimization. The cloud computing resource distributing method includes the steps that a cloud computing experiment platform is deployed; cloud computing resource distribution describing and modeling are conducted; different energy consumption optimization factors are selected; simulation platform construction is conducted; a test experiment is conducted on performance; model correction is conducted; a performance simulation experiment is conducted; a performance analysis model and key factors are determined; an energy consumption optimizing model for resource distribution under the cloud computing environment is studied; a heuristic resource distribution algorithm for achieving load balancing and meeting energy consumption minimization is designed; a performance simulating and testing experiment is conducted. According to the cloud computing resource distributing method based on the energy consumption optimization, the practical situation of resources under the cloud computing environment can be accurately and truly reflected, the energy consumption optimizing model is provided, and the limitation that only user task time demands are considered in an existing research method is broken through. In addition, the heuristic resource distribution algorithm achieving resource load balancing and energy consumption optimization is provided, and the limitation that global and local search balancing cannot be ensured in an existing resource distribution algorithm is broken through.

The invention discloses a robust resource allocation algorithm of a cognitive heterogeneous wireless network and belongs to the technical field of communication cognitive heterogeneous wireless networks. The robust resource allocation algorithm is characterized by considering a robust resource allocation algorithm in a cognitive heterogeneous multi-network environment influenced by channel parameter perturbation, considering cross-layer interference constraint and bounded channel uncertainty and interference power uncertainty in an objective function, converting the original infinite dimensional multivariate optimization problem into the convex optimization problem of certainty by using a robust optimization theory, obtaining a robust power control algorithm by using a lagrangian duality method, and achieving sharing of the robust resource of the cognitive heterogeneous wireless network. Simulation results show that the algorithm can effectively protect the performance of users of a macro-cellular network and has stronger robustness.