243 results about "Resource allocation (computer)" patented technology

The invention discloses a multi-protocol dynamic adaptation and session management method based on TCP / IP (Transmission Control Protocol / Internet Protocol), and belongs to the technical field of computer network communication. Comprising the following steps: S1, receiving TCP / IP connection from a client, and identifying a specific application layer protocol type through a protocol feature code; s2, dynamically loading an adapter of a corresponding protocol based on the protocol type identified in the S1, decoding original data into a unified intermediate format, and realizing protocol-independent data processing; s3, on the basis of decoding in the S2, different protocols are connected and bound to the same session object based on the device identifier, and cross-protocol sharing of the authentication state and the context is achieved; and S4, monitoring a network quality index based on the session binding in the S3, and dynamically allocating a high-priority service to the optimal protocol channel. According to the method, the problems of poor multi-protocol compatibility, isolated session management, unreasonable network resource allocation and the like are solved, and the flexibility, the reliability and the resource utilization rate of the system are remarkably improved.

The invention discloses a kubernetes cluster resource adjustment method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: collecting a target index, and analyzing the target index to construct a resource demand portrait of an application program; the target indexes comprise performance indexes, resource indexes and state indexes of all nodes and application programs of the kubernetes cluster; the resource demand of each application is predicted, when the node resource pressure index exceeds a target threshold value, a scheduling strategy is determined based on the resource demand and the resource demand portrait, and the to-be-migrated Pod is scheduled to a target node based on the scheduling strategy to complete resource adjustment; if all the nodes in the cluster do not meet the resource demand, calling a pre-registered node pre-registered to the cluster based on a preset node resource pool, adjusting the schedulable state of the pre-registered node, expanding the capacity of the cluster according to the adjusted node, and adjusting the resource based on the expanded cluster. And dynamic adjustment of resource allocation is realized.

The invention provides an operation and maintenance alarm intelligent filtering and grading processing method based on a self-adaptive algorithm, and relates to the technical field of computer operation and maintenance management, and the method comprises the steps: obtaining an alarm event, extracting semantic and time sequence characteristics, carrying out the dynamic clustering through employing a self-adaptive similarity measurement mechanism, calculating a priority score based on an influence range and an emergency degree, and carrying out the calculation of an alarm result. And a self-adaptive filtering strategy is implemented in combination with the operation and maintenance resource state, and finally, filtered alarms are distributed to corresponding operation and maintenance units for processing, collection and feedback. According to the method, redundant alarms can be effectively reduced, resource allocation is optimized, and the operation and maintenance efficiency and the alarm processing accuracy are improved.

The invention relates to the technical field of computers, in particular to a flow limiting method and system based on an AI gateway and a micro-service gateway, a medium and equipment, and the method comprises the steps: receiving service flow data corresponding to the micro-service gateway, AI flow data corresponding to the AI gateway, and a system resource utilization rate, building a quota negotiation channel between the micro-service gateway and the AI gateway, and transmitting the quota negotiation channel to the micro-service gateway; constructing a data set; calculating the flow quota of the micro-service gateway and the AI gateway through a dynamic quota algorithm based on the data set; receiving and identifying a service scene priority corresponding to the target request, and reserving an elastic burst quota for the target request based on the service scene priority; and judging the relationship between the total number of the current effective requests and the traffic quota of the corresponding gateway, and triggering a corresponding process. By establishing a dynamic quota mechanism and constructing a double-gateway cooperation mode of an AI gateway and a micro-service gateway, insufficient overall resource utilization efficiency caused by local current limiting is avoided, optimal global resource allocation is realized, and system stability is improved through cascade backoff.

The invention provides a task processing method, device and equipment for a server cluster, a computer program product and a computer readable storage medium. The method comprises the steps that a to-be-scheduled target calculation task is received, and the target calculation task comprises the resource demand quantity; performing resource matching on the resource demand quantity and the resource information of each server, and determining a plurality of candidate servers from the plurality of servers according to a resource matching result; determining a target server from a plurality of candidate servers according to the historical scheduling record of each candidate server; a task scheduling instruction is sent to a target server, and the task scheduling instruction indicates that the target calculation task is scheduled to the target server. By means of the method and device, efficient scheduling of server cluster tasks and reasonable resource allocation can be achieved, and the overall processing efficiency is improved.

The invention relates to the technical field of computer-aided material design, in particular to a material chemical formula generation method based on performance sensitivity self-adaptive stratified sampling, which comprises the following steps: inputting an element list; querying a performance sensitivity knowledge base to obtain a sensitivity level and an adjustment factor; analyzing the composite elements, extracting multi-dimensional element features, and quantitatively calculating element combination complexity; based on the element number, the sensitivity and the complexity, a corresponding sampling strategy is adaptively selected, and the sampling amount is dynamically allocated; generating a chemical formula and de-weighting; and outputting a chemical formula list and full-process metadata. Through innovation of resource allocation driven by performance sensitivity, composite element atomic-scale analysis, feature space clustering and the like, on the premise that sampling representativeness and chemical rationality are guaranteed, the calculation efficiency is improved by dozens of times to hundreds of times, the response time is reduced to the second level or the minute level, and the method has the advantages of being traceable, extensible and high in universality and has wide application prospects. And the material screening and discovery process is effectively accelerated.

The invention belongs to the technical field of artificial intelligence, and relates to a robot-based task processing method and device, computer equipment and a medium, and the method comprises the steps: carrying out the demand analysis of a task instruction, and obtaining task demand data; analyzing the task demand data and the collected hardware state data and environment characteristic data of the robot to generate a demand instruction; performing module splitting and priority ranking processing on a core algorithm of the robot based on the demand instruction to obtain a modular algorithm structure and priority data corresponding to the sub-modules; optimizing the modular algorithm structure based on the priority data and a strategy library to obtain a target modular algorithm structure; generating a resource allocation scheme based on the target modular algorithm structure and the hardware resource condition; and performing task execution processing based on the target modular algorithm structure and the resource allocation scheme. The task processing method and device can be applied to task processing scenes in the financial science and technology field and the digital medical field, and the task processing accuracy can be improved through the task processing method and device.

The invention discloses a heterogeneous computer and system for dynamically optimizing cache resources and a cache allocation method. The heterogeneous computer comprises a hardware performance monitoring unit, a main controller, a plurality of heterogeneous computing units and a shared cache unit. The hardware performance monitoring unit detects the utility gradient of the heterogeneous computing units based on a perturbation and observation method, the utility index and the cache resource allocation amount of each heterogeneous computing unit are collected before and after the cache resource allocation amount is changed, and the utility gradient is the utility index variation corresponding to the unit cache resource allocation amount variation; if the utility gradient is not smaller than a preset first threshold value, the main controller increases the cache resource allocation quantity of the heterogeneous computing unit; and if the utility gradient is not greater than a preset second threshold value, the main controller maintains or reduces the cache resource allocation quantity of the heterogeneous computing unit. According to the method, dynamic adaptation, intelligent scheduling and efficient utilization of cache resources can be realized, the execution efficiency of high-priority tasks is guaranteed, and the performance loss caused by cache competition and conflict is reduced.

The invention relates to the technical field of artificial intelligence and brain-computer interfaces, and discloses a brain-computer interface method and system based on dynamic hierarchical splitting federated learning and a medium, and the method comprises the steps that a client reports a local state; the reinforcement learning agent dynamically decides a joint action for each client based on the global state; the client calculates and uploads an intermediate activation value to the central server; and the server completes residual calculation and returns the gradient, and the client and the server update the subnet parameters respectively. According to the method, joint optimization is carried out on split layer selection and wireless resource and computing resource allocation through reinforcement learning, dynamic self-adaptive decision is realized by taking comprehensive reward of model identification precision, training time delay and client energy consumption as a target, communication overhead and training time delay are remarkably reduced on the premise of strictly protecting user data privacy, and user experience is improved. The terminal energy consumption is reduced, and the training efficiency and the final recognition performance of the model in the dynamic heterogeneous network environment are improved.

The invention discloses a computer running state intelligent management method and system, and belongs to the technical field of computer supervision. According to the method, multi-source data are collected and fused in real time, and a structured state data set is constructed; establishing a dynamic operation state portrait based on the data set and the system knowledge graph, and pre-judging the operation state of the next monitoring interval through a mixed time sequence prediction model; an unsupervised deep contrast learning two-way detection mechanism is adopted to identify abnormity, and a potential security attack event is identified in combination with an attack chain knowledge base and a causal inference engine; calculating a comprehensive priority according to the prediction state, the abnormity and the attack event, and dynamically executing linkage adjustment of computing resource allocation and network security protection; and monitoring a linkage adjustment effect and feeding back a deviation value between the linkage adjustment effect and a preset target to each model to realize full-process adaptive optimization. According to the method, resource dynamic accurate allocation and security threat active defense are realized, the system operation efficiency and protection effectiveness are improved, and the method adapts to the dynamic change of the computer operation state and threat.

A method for quality of service (QoS) flow control dynamically optimizes network resource allocation based on terminal power consumption status. An access network function receives terminal status information from an application function (AF) and determines a QoS profile for associated QoS flows from alternative profiles. The selected QoS profile is transmitted to a core network function or terminal for QoS updates. The terminal status information, which may include battery level, CPU load, or overheating status, informs the selection process to balance performance and efficiency. Core network functions manage QoS updates by interacting with the AF, access network, and terminals. The method, apparatus and computer readable medium ensure adaptive and energy-efficient QoS management for improved user experience and resource utilization in diverse network environments.

The invention relates to an elastic distributed computing power resource control method and system driven by low-carbon energy, and belongs to the technical field of computer distributed systems and green energy management. The method comprises the following steps: acquiring load data of distributed computing power nodes, identifying a server resource use signal, analyzing a carbon efficiency mapping relation between the nodes through an energy consumption weight detection method, and constructing an energy-computing power characteristic incidence matrix; extracting a unit energy consumption computing power output coefficient of each computing power node, performing low-carbon computing power resource priority scheduling through an elastic task allocation algorithm, and generating a node resource allocation instruction; establishing a distributed autonomous control model, and correcting a node execution state through an energy-computing power coupling feedback mechanism to obtain a distributed computing power resource balance interval; and establishing an elastic distributed computing power resource closed-loop control system, performing self-adaptive correction on task allocation weights, and controlling energy-task cooperative compensation between computing power nodes according to energy consumption feedback of the computing power nodes.

This invention discloses an intelligent resource allocation method for energy-aware partitions, belonging to the field of computer architecture and operating system task scheduling technology. The method divides computing resources into multiple independent energy-aware partitions based on the processor cores. It then monitors the energy status parameters of each partition in real time. By parsing the instruction code stream of the tasks to be assigned, the proportion of arithmetic and logical instructions is extracted as instruction mixing features, and the address span of memory access instructions is analyzed to obtain cache access density features. Based on the energy status of the partitions and the computation and memory access characteristics of the tasks, a suitability score for the task relative to each partition is generated, comprehensively reflecting the energy matching degree and hardware resource matching degree. Based on the suitability score and the remaining task acceptance capacity of the partitions, a two-dimensional assignment strategy is used to dynamically map tasks to the core execution in the optimal energy-aware partition. This invention achieves fine-grained matching of computing resources and energy status, optimizing system energy utilization efficiency.

The invention discloses a resource allocation method, electronic equipment and a readable storage medium, and belongs to the field of computers. The method comprises the following steps: determining at least one second object matched with a first target object from a plurality of second objects; the first target object is any one of a plurality of first objects; under the condition that the at least one second object comprises a second target object and at least one first object matched with the second target object comprises the first target object, allocating a to-be-allocated resource to a physical node; the second target object is any one second object in the plurality of second objects; wherein the first target object is one of the to-be-allocated resource and the physical node, the second target object is the other one of the to-be-allocated resource and the physical node, and the to-be-allocated resource is a resource operated by the physical node.

The present invention extends to methods, systems, and computer program products for optimizing resource allocation in view of predicted network traffic patterns and predicted power consumption. Network packets defining a network traffic flow can be received at a platform over time. Metrics can be derived from one or more applications executing on resources of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A resource adjustment model can be trained using the model training data. Executing the model can be automated to adjust resource allocation at the platform. Additional network packets defining an additional network traffic flow can be received at a platform over time. Data contained in the additional network packets can be processed using the adjusted resource allocation.

The invention provides a communication method, device and system, a computer program product and a readable storage medium, and the method can be applied to a random access scene exemplarily. In the method, a terminal device receives first information sent by an access network device; the first information is used for configuring at least two sets of communication resources, and the at least two sets of communication resources are both used for transmitting a channel state information report. The terminal equipment receives second information sent by the access network equipment; the second information is used for triggering a channel state information reference signal, and the second information is used for indicating a resource identifier of a target resource, and the target resource is one of the at least two sets of communication resources. And the terminal equipment sends the channel state information report to the access network equipment based on the target resource. According to the method, the certainty and timeliness of communication resource allocation of the channel state information report can be effectively improved.

The invention relates to a report processing method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: constructing and deploying a configuration table system in a business database, wherein the system comprises a job configuration table, a job dependency table, a report parameter table and a front-end display configuration table; and performing semantic analysis on the historical task logs of the batch jobs recorded in the job configuration table through the large language model, and outputting a dynamic scheduling strategy which is used for adjusting the initial execution frequency and the resource allocation quantity of the batch jobs in the job configuration table. By identifying entity information in a user service query instruction and matching an optimal chart type, accurate conversion from a database field to a front-end display field is completed in combination with a front-end display configuration table. And finally, an interactive visual chart is generated. By adopting the method, the data change rule can be intelligently analyzed, the business decision-making efficiency is improved, and meanwhile, the comprehensive cost of report development, operation and maintenance is effectively reduced.

The invention discloses an input and output scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of computers.According to the input and output scheduling method and device, the future load state of a storage system is predicted based on historical input and output request data, and the response sequence and the resource allocation strategy of requests with different priorities are dynamically adjusted accordingly; therefore, a mapping relation between load prediction and storage medium characteristics is established, so that the technical problems that key request delay is uncontrollable and system throughput fluctuates when the load suddenly increases in a mixed read-write load scene can be solved; the technical effects of ensuring the low-delay response of the key request, stabilizing the system throughput and improving the overall performance and stability of the system are achieved.

The invention discloses a user interface preprocessing method and device based on behavior prediction, a storage medium and computer equipment. The method comprises the following steps: monitoring an interaction behavior event flow in a current user interface in a client; generating an interaction behavior feature vector based on the interaction behavior event flow; performing interaction behavior prediction according to the interaction behavior feature vector through a pre-trained machine learning model, and determining a next-step interaction component and a confidence coefficient of the next-step interaction component; and determining a preprocessing operation type of the next-step interaction component according to the confidence coefficient, and executing a preprocessing operation of the next-step interaction component based on the preprocessing operation type. According to the method, the defects of single prediction dimension and extensive resource allocation of the traditional preprocessing technology are overcome, the conversion from static rule driving to dynamic intelligent prediction is realized, and the intelligent and efficient utilization of system calculation and network resources can be ensured while the interface response speed and smoothness can be improved.

The application discloses a device for production of work station automatic configuration, relates to the technical field of notebook computer production, and discloses a device for production of work station automatic configuration, which comprises the following steps: task scheduling, resource allocation, process parameter setting, quality control, data acquisition and analysis, detection and classification; visual appearance detection is performed on completed equipment, and the equipment is classified; the device for production of work station automatic configuration comprises a base, a transmission mechanism, a material taking mechanism and a detection mechanism are arranged above the base, and a material distributing mechanism is arranged on the top surface of the base; in the application, the transmission mechanism is arranged to transmit a carrier loaded with a notebook computer, then the material taking mechanism is arranged to take the notebook computer, then the detection mechanism is arranged to visually detect the notebook computer, and finally the material distributing mechanism is arranged to classify unqualified products, so that the visual detection of the notebook computer is automatically achieved.

This application provides a recommendation method, apparatus, and computer-readable storage medium based on multi-objective optimization. The method includes: acquiring user rating information, including ratings from multiple users for multiple items; clustering users into different clusters using K-means clustering based on the user rating information; predicting the predicted ratings of users for items in each cluster based on a probability propagation algorithm with improved resource allocation; generating an initial population in each cluster based on the predicted ratings of users for items; and solving a multi-objective optimization problem based on an accuracy objective function and a diversity objective function using an evolutionary strategy guided by accuracy-preference users, thereby obtaining the target recommendation result for each cluster. By using the above method to guide the evolutionary direction of the algorithm through user preferences, the target recommendation result can be made more biased towards the accuracy objective function while taking into account diversity.

Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

The invention relates to the technical field of data processing, and provides a data task scheduling method and system and a computer readable storage medium. The method comprises the following steps: constructing an initial task graph model according to a task definition; dynamically receiving a topology change instruction to perform online adjustment on the current task graph model; performing data consistency verification on the adjusted task graph model; after verification is passed, effective switching of the adjusted new task graph model is achieved, and the task being executed in the system is not interrupted in the effective switching process; task scheduling and resource allocation are carried out based on the new task graph model and the heterogeneous resource state after the new task graph model takes effect, and a target execution task adaptive to the heterogeneous environment is determined; and executing the target execution task in the heterogeneous environment. A dynamic topology maintenance mechanism is introduced, a topology change instruction is received and responded in real time, and finally seamless effective switching of a new model is realized, so that the problem of service interruption caused by static topology stiffness of a traditional system is solved, and the flexible adjustment capability of coping with global topology is improved.

The application discloses a kind of based on flow perception intelligent network interface card optimization system and method, it belongs to computer network technical field.The optimization system of the application includes flow perception module, flow prediction module, resource scheduling module, priority queue module and load balancing module.The application introduces resource monitoring, flow perception, resource prediction and dynamic scheduling module, real-time monitoring network flow and predicting its change trend, according to flow mode dynamically adjusts resource allocation.The application effectively solves the problem of resource contention, through priority ordering and load balancing, ensure the stable operation of high priority task, improve the throughput and stability of system, reduce resource waste.The application is suitable for resource management of intelligent network interface card in data center and cloud computing environment, improves network processing efficiency and resource utilization, guarantees service quality, reduces resource waste, and enhances the adaptive capacity of system to dynamic load and complex flow mode.

The invention discloses a computer network management method, which relates to the technical field of computer network management, and is characterized in that three groups of algorithm units in a computing processing module are matched with one another to jointly form a core architecture of the computer network management method; a plurality of influence factors such as bandwidth usage amount, data packet quantity and data packet size can be comprehensively considered, real-time network performance indexes of different users are calculated, a task with the shortest execution time is selected to be executed preferentially according to the network performance indexes and a short job priority (SJF) algorithm, network resource distribution of the multiple users is completed, and the user experience is improved. The efficiency of network resource allocation is improved, and the fairness of network resource management and allocation is ensured; and when the user carries out secondary broadband utilization rate calculation, the network performance index calculated last time is substituted into the formula to carry out iterative calculation, a new network performance index is obtained, and network resource allocation is optimized and adjusted in real time according to the new network performance index.

A computer-implemented method is disclosed. The method includes: receiving, via a graphical user interface of a resource account management application, a first request to change a value of a first attribute of a defined resource allocation; responsive to receiving the first request: generating a graphical indicator that includes a deep link to a function of the resource account management application for modifying a related second attribute of the resource allocation; and presenting the graphical indicator in a portion of the GUI that is accessed at the time of receiving the first request.

Systems, computer program products, and methods are described herein for network allocation and monitoring engine using artificial intelligence (AI) and dynamic mapping. The present disclosure is directed to extracting data from network data packets received from at least one or more data sources and uses an AI engine to identify network resources and network threats. The AI engine generates at least one network attribute set based on at least the network resources and network threats and determines and assigns a weight for each of the at least one network attribute set. The AI engine generates a directed graph based on at least the network attribute set, which is associated with at least one distributed network, and determines an internal network threshold based on at least the directed graph. The AI engine generates an internal distributed network resources allocation based on at least the directed graph and the internal network threshold.

This disclosure provides systems, methods, and apparatus, including computer programs encoded on a computer storage medium, for wireless communication. In one aspect of the present disclosure, a method performed by a user equipment (UE) for wireless communication includes assigning semantic feature data associated with a semantically encoded image or video frame into a plurality of portions according to a modulation and coding scheme (MCS) and resource allocation. The method includes performing hyper-priori decoding on compressed feature probability data associated with the semantic feature data. The method includes encoding each of the plurality of portions of semantic feature data in association with a respective portion of the plurality of portions of decoded feature probability data. The method includes transmitting a transport block (TB) including one or more of the encoded portions of the semantic feature data. Other aspects and features are also claimed and described.

The invention discloses a resource allocation method and device, a computer program product and electronic equipment. The method comprises the following steps: acquiring project information of a target project; multiple evaluation indexes of the target project are determined according to the project information, index weights of the evaluation indexes are determined based on an index judgment matrix, and matrix elements of the index judgment matrix are used for reflecting the importance proportion between every two evaluation indexes; the feasibility level of the target project is determined based on a fuzzy relation matrix and the index weight of each evaluation index, and matrix elements of the fuzzy relation matrix are used for representing the membership degree of each evaluation index to comments of different levels; and determining a cloud resource allocation strategy of the target project according to the project information and the feasibility level, and allocating corresponding cloud resources to the target project according to the cloud resource allocation strategy. The technical problems of unreasonable resource allocation and untimely risk response existing in a cloud resource scheduling strategy for formulating a project based on static information are solved.

Embodiments of the present application provide a resource allocation method, a storage medium, an electronic device and a computer program product. The method comprises: a first network device divides a first resource group into a first number of bandwidth part (BWP) units, wherein the first resource group comprises frequency domain resources of at least two frequency bands or frequency domain resources of at least two carriers, or the first number of BWP units is associated with the frequency domain resources of the at least two carriers; and the first network device allocates a second number of BWP units in the first resource group to a user equipment, wherein the second number is an integer greater than or equal to 1 and less than or equal to 256, and the second number is less than or equal to the first number. Through the embodiments of the present application, the intermediate step of allocating spectrum resources to BWP resource allocation is cancelled, the complexity of cross-band or cross-carrier frequency domain resource scheduling is reduced, the problem of high scheduling complexity and latency of BWP resources in the related art is solved, and the effect of improving the efficiency of BWP resource configuration is achieved.