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

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention discloses an industrial personal computer and multi-graphics card collaborative parallel computation acceleration system, which relates to the technical field of industrial resource allocation and parallel computation, and comprises a resource monitoring and predicting module, a resource management module and a prediction type resource preparation module, the task splitting and collaborative execution module comprises a task splitting module, a cross-node collaborative module and a collaborative operation engine; the intelligent scheduling and dynamic resource allocation module comprises an intelligent scheduler, a dynamic resource allocation module and a conflict avoidance module. According to the method, the GPU video memory utilization rate, the core utilization rate, the temperature, the video memory fragment rate, the available video memory total amount, the CPU core total utilization rate, the load condition and the idle core number index are collected in real time through a resource monitoring module, and the video memory capacity, the GPU core occupancy rate and the CPU load requirement are predicted in advance before a task is submitted in combination with a gradient boosting decision tree and a neural network prediction model; and resources are reserved, so that the scheduling delay is remarkably reduced, and the scheduling hit rate is improved.

The invention discloses a parallel processor dynamic resource allocation system and method based on reconfigurable hardware, and relates to the technical field of computer chips, and the system comprises a workload monitoring module which is responsible for monitoring the workload type and the resource demand of a task processed by a parallel processor in real time, and determining the task type and the resource demand by identifying the task type and the resource demand; a monitoring result is fed back to the resource allocation control module; the resource allocation control module is responsible for generating a corresponding resource allocation control signal based on feedback information of the workload monitoring module and dynamically configuring the reconfigurable hardware module; the reconfigurable hardware module is responsible for realizing efficient adaptation to diversified tasks through a plurality of reconfigurable units formed by programmable logic devices according to different working load dynamic reconfiguration functions and connection modes; and the data caching and transmission module is responsible for cross-module data circulation and global data storage. Different task requirements can be accurately adapted, and the resource utilization rate is improved.

The invention discloses a GPU Box hardware decoupling system based on a Retimer card and a PCIe Switch chip, and belongs to the technical field of computer hardware architecture and high-speed interconnection. According to the system, a Retimer card and a PCIe Switch chip are integrated in an independent GPU Box, and a decoupling link of a CPU server and a GPU acceleration card is constructed; the Retimer card realizes 30-meter long-distance PCIe signal transmission and breaks through physical distance limitation; the PCIe Switch chip pools GPU resources through a dynamic routing and MRIOV technology, supports flexible allocation of computing power by multiple servers, and realizes Peer-to-Peer direct connection communication between GPUs. Aiming at a large model reasoning scene, the system optimizes KV cache bandwidth allocation and video memory and memory cooperative scheduling, so that the 100B parameter model reasoning throughput is greatly improved; and meanwhile, the usability of the system is greatly improved through fault isolation and hot plug design. According to the method, the problems of physical binding of the CPU and the GPU, limited transmission distance, rigid resource allocation and the like in a traditional architecture are solved, and the method is suitable for large-scale AI calculation and distributed GPU cluster deployment.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention relates to a tensor core resource allocation method, computer equipment, a readable storage medium and a computer program product. The method comprises the steps of determining a target queue corresponding to a first target calculation instruction from an execution queue based on a priority corresponding to the first target calculation instruction under the condition of determining that the first target calculation instruction from a target thread bundle completes data synchronization, and writing the first target calculation instruction into the target queue, the execution queues comprise at least two execution queues corresponding to different priorities; under the condition that the tensor core resource is idle, the calculation instruction stored in the queue to be allocated is written into the tensor core resource to execute corresponding calculation, and the queue to be allocated is the queue with the highest priority in the execution queues storing the calculation instruction. By adopting the method, the resource utilization rate can be improved.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention belongs to the field of computer resource management, particularly relates to a computer resource dynamic allocation management method based on cloud computing, and aims to solve the problems of locality of a decision view angle and hysteresis of a time dimension in related technologies. The method comprises the following steps: acquiring the resource utilization rate of each virtual machine in a cloud environment, wherein the resource utilization rate comprises a historical resource utilization rate and a real-time resource utilization rate; according to the resource utilization rate, predicting a resource demand of each virtual machine in a future preset time period; determining a resource competition conflict according to the predicted resource demand, and constructing a resource competition graph model; mapping the resource competition graph model into a non-cooperative game model; and determining a scheduling scheme in the cloud environment based on the non-cooperative game model, and executing a resource allocation operation according to the scheduling scheme. According to the method, potential conflicts can be identified and scheduled before resource competition actually occurs, and performance reduction caused by insufficient resources is reduced.

The invention discloses a data pre-reading method and device, equipment and a storage medium, and relates to the field of electronic computers.In the data pre-reading method, data heat, a load mode and a historical read command proportion are determined by analyzing historical access information, cache operation is judged based on the data heat, and a target hard disk area is predicted based on the load mode; and adjusting the size of a pre-read cache space based on the historical read command proportion to realize pre-read and cache management. By optimizing the data pre-reading strategy and cache resource allocation, the pre-reading precision of the data can be improved.

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.

A computer-implemented system and method for dynamic, explainable, and adaptive prioritization of cybersecurity controls is disclosed. The system ingests unstructured threat reports and employs a natural language processing (NLP) module to automatically extract adversary tactics, techniques, and procedures (TTPs). A scoring module applies a mathematical time-decay function to the extracted intelligence. A novel hybrid prioritization engine provides explainability-by-design by computationally integrating these objective, data-driven scores with organization-specific context within a transparent multi-criteria decision analysis (MCDA) model. Critically, the system establishes a self-optimizing closed feedback loop; it receives real-world control effectiveness metrics from the operational environment and uses this data as new ground-truth labels to continuously and automatically retrain internal machine learning models. This adaptive mechanism improves the computer's own predictive accuracy and resource allocation efficiency over time, representing a tangible technical improvement.

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 discloses a computer resource scheduling optimization method based on artificial intelligence, and relates to the technical field of computer resource allocation, and the method comprises the steps: constructing a twinborn computer space, marking twinborn collection ends, and collecting resource use data; constructing an original coordinate system according to the resource use data and performing state generation to obtain a resource use state diagram; performing traversal check on the resource use state diagram to obtain a periodic fluctuation segment, and performing resource quantification through a twin computer space to obtain a fluctuation proportion accommodating frame; monitoring and deploying the CPU based on the twinborn computing space to obtain virtual deployment resources, applying the virtual deployment resources to the computer, and generating an optimal scheduling strategy according to the application process; resource allocation is optimized, the CPU utilization rate is improved, computer operation pressure is reduced, and idle or overuse of resources is avoided.

The invention relates to a resource task dynamic self-adaption method, in particular to a resource task dynamic self-adaption method based on an OpenHarmony MCP and a DeepSeek large model. The method aims at solving the problems that a traditional operating system and OpenHarmony are insufficient in dynamic adaptability, resource utilization rate, fault prediction and user experience optimization in the complex and changeable environment, especially in the distributed and heterogeneous environment. According to the method, OpenHarmony system resources and application program task states are monitored in real time through the MCP module, and data are standardized; and the DeepSeek large model analyzes the data and generates an optimization instruction which is used for dynamically adjusting resource allocation and task scheduling in the OpenHarmony environment. In addition, the system concept further comprises functions of fault prediction and recovery, user interaction optimization and the like. According to the method, the resource utilization rate, the task scheduling efficiency and the user experience of the OpenHarmony system in a complex scene are improved, and meanwhile, the technology is kept autonomous and controllable. The invention belongs to the technical field of computer operating systems.

The invention provides a computer system starting method and device, electronic equipment and a storage medium, and can be applied to the technical field of computers. The method comprises the steps of executing a first initialization task under the condition that power-on of a computer system is completed; the first initialization task comprises executing an initialization configuration operation on first equipment of the computer system; under the condition that the execution of the first initialization task is completed, based on the global resource information of the equipment resources, allocating the equipment resources to the plurality of processors, and determining local resource information of each of the plurality of processors; executing the plurality of second initialization tasks in parallel by utilizing the plurality of processors, and determining respective initial resource allocation results of the plurality of processors; under the condition that execution of the multiple second initialization tasks is completed, resource unification adjustment is executed according to the multiple initial resource allocation results, and a target resource allocation result is obtained; and in response to resource adjustment completion, loading an operating system of the computer system to start the computer system.

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 an intelligent scheduling method and system for concurrent requests of a multi-tenant cloud computer operation management platform, belongs to the technical field of AI intelligent application, and aims at solving the technical problem of how to overcome the defects of resource preemption and low service quality of the multi-tenant cloud computer platform in a high-concurrency scene. According to the technical scheme, the resource utilization efficiency and the service quality guarantee capability of a multi-tenant cloud computer platform are improved, and the method comprises the steps of tenant behavior intelligent analysis: collecting related original data of each tenant in real time, constructing a tenant portrait model, and achieving multi-dimensional priority dynamic evaluation; resource demand time sequence prediction: predicting resource demands of a CPU, a GPU and a memory in a set period through a Prophet prediction model in combination with historical data and a real-time load, establishing a resource demand early warning mechanism, and performing resource pre-allocation in advance; an elastic resource allocation strategy is established, resource sufficiency and shortage scenes are distinguished, dynamic resource quota adjustment is achieved, and response time is shortened; and abnormal behaviors are monitored in real time and rapidly isolated.

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 provides an intelligent blue light storage method, system and device based on S3 storage and a medium, and belongs to the field of computer data storage, and the method comprises the following steps: recording business file data to a cache region through an S3 interface; counting a business data record table and a data log table in a specified period; identifying hot data and cold data in the business data record table based on the data log table in the specified period; periodically identifying and marking the hot data and the cold data by using the S3 storage type; intelligent storage resource allocation processing is carried out based on the hot data; intelligent distribution processing of storage resources is carried out based on the cold data, and the storage resources are packaged and stored in a blue-ray disc jukebox; and S3, based on the intelligent distribution system stored in the S3, performing reading operation on the cold data on the optical disc. According to the method, the problem of single received data of traditional blue light storage is solved, automatic and precise classification of cold and hot data is realized, manual subjective judgment errors are avoided, the hot data access efficiency is improved, and the storage performance is optimized.

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.

An autoscaling method and apparatus in a Kubernetes cluster, which method and apparatus are applied to the technical field of computers. The method comprises: in historical monitoring data, using an interpolation method to impute missing data between adjacent data points to obtain preprocessed sequence data; performing a fast discrete Fourier transform on the preprocessed sequence data to obtain a spectrogram of original data; analyzing the spectrogram, and identifying candidate periods in the spectrogram; using a preset correlation coefficient calculation function to verify the candidate periods to obtain a dominant period; on the basis of the dominant period and operation data in a cluster, performing peak prediction; and when a predicted peak exceeds a preset autoscaling threshold, triggering a scale-up or scale-down operation corresponding to a system. By means of the present disclosure, the requirement for resources in a period of time in the future can be predicted, and scaling up or scaling down can be performed, so that a more active and accurate resource allocation policy is realized, and the stability of a service is ensured.

The invention discloses a dynamic multi-thread memory access management method, and relates to the technical field of computers, in particular to a dynamic multi-thread memory access management method and device.The method comprises the following steps that a plurality of memory access requests outside a processor and / or inside the processor are obtained, and a first request queue is formed; wherein the memory access request comprises a thread identifier, an access type and a target memory address; dynamically sequencing the memory access requests in the request queue according to a preset priority rule, and calculating the memory access request with the highest priority; wherein the priority rule comprises at least one of a thread priority rule, a request source priority rule and a memory address priority rule; obtaining and executing the memory access request with the highest priority; the memory resource allocation efficiency can be effectively improved, the system adaptability and flexibility are enhanced, the hardware implementation complexity is simplified, and the response delay is reduced.

The invention discloses a computer resource allocation method and system based on big data analysis, and relates to the technical field of computers, and the method comprises the steps: collecting computer resource data in real time, carrying out the preprocessing, and predicting the future resource demands of a computer through a reinforcement learning model based on the preprocessed data; based on future resource requirements, a computer resource allocation strategy is generated by combining a particle swarm optimization algorithm with a cloud model disturbance mechanism and self-adaptive inertia weight adjustment, and a dynamic scheduling mechanism of cloud computing and edge computing is utilized. According to the method, key parameters of the particle swarm optimization algorithm are set and initialized, and adaptive inertia adjustment, cloud model disturbance and an auxiliary particle replacement mechanism are combined, so that the global search capability and diversity control of the particle swarm are enhanced, local optimum is effectively avoided, the quality and convergence stability of a resource allocation solution are improved, and the resource allocation efficiency is improved. Therefore, the accuracy and efficiency of computer resource allocation are remarkably 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.