81 results about "Resource virtualization" patented technology

The invention relates to the technical field of Internet of Things management, in particular to a lightweight Internet of Things management system based on a star flash protocol stack, which comprises a plurality of modules such as a star flash protocol communication module, a dynamic topology management module and a resource virtualization module. The satellite flash protocol communication module realizes low-power-consumption efficient connection of equipment; the dynamic topology management module optimizes the network topology by using an improved multi-agent Q learning algorithm; the resource virtualization module realizes accurate resource allocation through a weighted bipartite graph matching model; the safety protection module adopts an attention mechanism to detect abnormities and guarantee data safety; the edge co-processing module realizes intelligent task unloading by means of deep reinforcement learning; all the modules cooperatively work under the overall planning of the cross-module coordination controller, and the information barrier is broken. According to the invention, the problems of high communication energy consumption, poor resource allocation, weak security protection, low task processing efficiency, insufficient module collaboration and the like of a traditional Internet of Things system are effectively solved, the system communication efficiency, the resource utilization rate and the security are remarkably improved, and the task processing capability is enhanced.

The invention relates to a multimodal transport end-to-end supply chain collaborative management method based on container logistics, and belongs to the technical field of supply chain management. The method comprises the following steps: virtually integrating scattered cargo owner demand, carrier transport capacity and transit point operation capability resources through a cloud computing platform to form a shared resource pool, and constructing a dynamic collaborative network; performing intelligent matching and scheduling on the shared resource pool through a contribution degree distribution mechanism to obtain a supply chain full-link state; dynamic routing optimization is carried out according to the full-link state of the supply chain, and optimal path selection is carried out on the transportation mode of container logistics based on the business process of multimodal transportation; a task is automatically decomposed through an intelligent contract and issued to a node, a carrier automatically uploads a voucher through an RFID gate after completing the node task, and the contract verifies the voucher and then triggers a next node task. Seamless connection and efficient collaboration among all nodes of the supply chain are achieved, and the transportation efficiency and reliability of container logistics are improved.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention discloses an integrated machine room operation and maintenance management system, which relates to the field of information technology management and comprises a plurality of modules such as a distributed data acquisition module, a heterogeneous equipment adaptation module and a standardized communication interface module. The system realizes module cooperation through a two-way synchronous lock mechanism and an intelligent load sensing mechanism; the distributed acquisition module realizes millisecond-level high-precision data synchronization by using an ultra wide band and millimeter wave combined sensing technology; the heterogeneous equipment module eliminates communication compatibility obstacles; the standardized communication module is fused with an optical fiber and a dynamic arbitration mechanism to realize microsecond-level low-delay communication; the resource virtualization module supports unified abstraction and low-delay scheduling of physical resources; the expansion module can complete new node integration within three seconds; the fault-tolerant module can early warn faults ten minutes in advance and realize quick recovery; the intelligent decision-making module optimizes an operation and maintenance strategy based on edge reinforcement learning; and the interaction module supports multi-terminal state reproduction and programming control. The method has the beneficial effects that automation, intelligence and reliability levels of machine room operation and maintenance are improved.

The invention provides a resource intelligent bus and task parallel hydrogen energy data scheduling optimization method and system, relates to the technical field of energy, and aims to solve the problems of resource heterogeneity, lack of real-time performance and dynamic adjustment, insufficient energy efficiency optimization, poor adaptability to abnormal working conditions and the like in the prior art. According to the method, a resource intelligent bus architecture is constructed, and the resource intelligent bus architecture comprises a resource virtualization encapsulation layer, a resource state sensing layer and a global prediction and scheduling layer; in the resource state sensing layer, performing multi-granularity task decomposition on hydrogen energy business requirements, and establishing an inter-task dependency relationship model by adopting a directed acyclic graph; in the global prediction and scheduling layer, a multi-objective optimization model is constructed based on a dependency relationship between resource states and tasks, and resources and tasks are dynamically matched according to an optimal solution of the model; and dynamically scheduling the resources according to a matching result of the resources and the tasks. The problems in the prior art are solved, and the hydrogen energy data scheduling optimization performance is improved.

The invention discloses a parallel task scheduling method based on node dynamic load and task relevance measurement. The parallel task scheduling method comprises the following steps: constructing a global scheduler for computing resource virtualization, sample allocation based on reinforcement learning and a local scheduler based on hybrid drive model scheduling on a server; in the preprocessing stage, computing resources are counted, and computing resource virtualization, GPU virtualization multiplexing technology, GPU acceleration and the like are carried out; a global scheduling stage oriented to test task parallel optimization: performing sample scheduling feasibility analysis containing fuzzy time, computational node selection based on an EXP3 algorithm, and sample scheduling oriented to node load; and a multilevel model parallelization stage based on domain knowledge and data hybrid driving, wherein a model cohesion coupling degree measurement mechanism, a model coupling mode knowledge base and a multilevel model parallelization method are included. According to the method, the global scheduler and the local scheduler are comprehensively utilized, the parallelism degree between samples and parallelism between models in the samples are achieved, and the method has wide application value and use prospects in all fields.

The invention provides a GPU (Graphics Processing Unit) resource virtualization computing power scheduling method. The method comprises the following steps of: obtaining a task and determining a computing demand characteristic and a task attribute of the task; determining a resource demand according to the calculation demand characteristic and the task attribute; determining task priorities according to the task attributes; and determining a scheduling result of the GPU resource according to the task priority and the resource demand. The GPU resource of the task is allocated through the specific task attribute, the high efficiency of GPU operation is ensured, the computing power resource is allocated in a balanced manner, the utilization rate of the GPU is improved, the balance of the task load is ensured, and the task is completed more efficiently.

The invention relates to the technical field of computing power cabin cluster scheduling. The invention relates to a cluster scheduling control method based on a computing power cabin. The method comprises the following steps: S1, obtaining performance data of a computing power cabin, and then carrying out resource virtualization to form a resource pool; s2, collecting historical operation data and real-time operation data of the service user, and predicting a resource demand of the service user to obtain a predicted resource demand; s3, combining the predicted resource demand with the real-time operation data to carry out predicted volatility analysis, setting an isolation fluctuation threshold value, then comparing the predicted volatility with the isolation fluctuation threshold value, and when the predicted volatility is smaller than the isolation fluctuation threshold value, judging whether the predicted volatility is smaller than the isolation fluctuation threshold value or not; according to the method, resource demand prediction is carried out by using the historical operation data and the real-time operation data, resources can be allocated to the user more accurately, and when the predicted fluctuation rate is smaller than the isolation fluctuation threshold value, the resource instance is automatically started to carry out node isolation on the user, so that the stability of resource allocation is ensured.

The invention provides an IP network acquisition and control plug-in implementation method and device based on a Matrix command adapter, and relates to the technical field of network communication, and the method comprises the steps: analyzing and converting a control instruction of heterogeneous network equipment through the Matrix command adapter, and generating a standardized intermediate instruction set; deploying a resource virtualization pool in the IP network acquisition and control plug-in, and dynamically allocating network bandwidth, computing resources and equipment interfaces; and matching the middle instruction set with the execution capability of the target equipment in real time, and generating an executable equipment control instruction. According to the method, heterogeneous network equipment instructions can be flexibly analyzed and converted through the dynamic protocol adaptation layer, new equipment and new protocols can be quickly adapted through template matching and self-defined script extension, the compatibility and expansibility of network management are improved, and the resource virtualization pool is combined with the prediction model and the dynamic priority queue, so that the network management efficiency is improved. The resources are intelligently allocated according to the real-time load and historical data of the equipment, the resource utilization rate is improved, operation of core service equipment is guaranteed, and the overall performance of the network is improved.

The invention relates to a software load computing resource virtualization allocation method and device, equipment and a medium. The method comprises the following steps: acquiring a task description vector of a task and real-time state data of computing resources; according to the task description vector of the task and a preset historical performance database, computing resources needed when the task is started are analyzed, and a starting feature vector is obtained; according to the starting feature vector and the real-time state data, calculating a computing power hotspot distribution diagram of the starting task; the computing power hotspot distribution map is used for representing the use condition of each computing resource at different time points when the task is started; based on the computing power hotspot distribution map, the task description vector and the real-time state data, simulating a competition condition when the task is started, and obtaining resource competition intensity quantitative data; generating a task pre-allocation scheme according to the resource competition intensity quantitative data and the real-time state data; the pre-allocation scheme is used for representing the allocation relationship between the task and each computing resource. By adopting the method, the computing power resource utilization rate can be improved.

The embodiment of the invention provides a quantum hardware resource virtualization method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target quantum bit unit topology for a target quantum computing task; obtaining a quantum bit unit network in the quantum hardware resource; in the quantum bit unit network, obtaining a plurality of candidate quantum bit unit sub-networks conforming to the target quantum bit unit topology; based on the self-operation fidelity of each quantum bit unit in the candidate quantum bit unit sub-network and the mutual operation fidelity between adjacent quantum bit units, determining the overall operation fidelity of the candidate quantum bit unit sub-network; and based on the overall operation fidelity, determining a target quantum bit unit sub-network as a virtualization resource for the target quantum computing task. According to the embodiment of the invention, the utilization rate of virtualized resources is improved, and relatively high operation accuracy is ensured. The embodiment of the invention can be applied to various scenes such as quantum calculation and superconducting quantum calculation.

The invention relates to a computing power resource virtualization isolation and multi-level scheduling method and system in a containerization environment, and the method comprises the steps: deploying an index collector on each working node of a Kubernetes cluster, and collecting the node computing resource utilization condition in real time; when a calculation task request submitted by a user is received, the Kubernetes API server performs field legality verification on the configuration file submitted by the user and stores the field legality verification; executing a computing power resource multi-level scheduling algorithm according to a configuration file submitted by a user and a resource utilization condition, and finding a working node with the highest adaptation degree as an optimal node; the user-defined scheduler sends an instruction to the optimal node, requires the optimal node to create a vGPU and a Pod, and mounts the vGPU to the corresponding Pod to run a calculation task; and recording vGPU distribution information of the optimal node, storing and updating the cluster. According to the invention, computing power resource sharing and isolation are realized, and the resource utilization rate is improved.

The invention discloses a coordinated regulation and control method for a comprehensive energy system to participate in multiple frequency modulation services of a power grid, and the method comprises the steps: collecting the operation state data of four resources, carrying out the preprocessing of the operation state data, obtaining the original data of the resources, constructing four resource virtualization energy storage models, and bringing the original data of the resources into the four models. The method comprises the following steps: respectively obtaining adjustable energy and power boundaries, constructing a virtual energy storage model, uniformly mapping four resources into a virtual energy storage data set through the virtual energy storage model, substituting the virtual energy storage data set into a multi-frequency modulation collaborative optimization model, obtaining an optimal scheduling result, and realizing model flexibility. And the coordination control between each subsystem in the IES and the power grid is realized, and the coordination operation mechanism of multiple auxiliary service markets is perfected.

The invention discloses a sensing, calculation and control integration-oriented resource virtualization and real-time scheduling method, and relates to the field of resource management.According to the method, heterogeneous processors with different architectures are subjected to standardized description by constructing a heterogeneous processor resource representation model, so that the heterogeneous processors can be perceived and managed in a machine space; on the basis, through a sensing, calculation and control integrated design, sensing, calculation and control multi-type task scheduling oriented to the heterogeneous multi-core processor is further supported; the isomerism of a processor and the operation characteristics of different types of containers are quantified through theoretical analysis, and the'resource '-'service quality + energy consumption expression' mapping relation among service quality, real-time performance, energy consumption and container allocation strategies, task processing proportions and processor operation frequencies is quantitatively described to construct a scheduling problem model. According to the method, the resource utilization rate of the processor can be improved, so that the real-time guarantee of sensing, calculating and controlling multiple types of tasks in a resource shortage scene is realized, and the overall service quality and energy consumption performance of the system are improved.

The invention discloses an edge intelligent hardware platform architecture method and system suitable for a high-mobility environment. The method comprises the steps of edge intelligent basic hardware environment preparation, edge calculation and transmission resource virtual pooling management, edge intelligent calculation container whole-network lightweight deployment and edge calculation and transmission resource container scheduling distribution. The system comprises edge intelligent servers for training, edge intelligent servers for reasoning, edge network communication equipment, edge calculation and transmission resource virtualization software, edge calculation and transmission resource receiving and management software and edge intelligent calculation container deployment service software which are deployed in edge service nodes and edge terminal nodes. And the edge intelligent computing container deploys agent software and edge computing transmission resource scheduling software. According to the method, unified description and management of calculation and transmission resources based on an edge intelligent basic hardware environment are supported, distributed and lightweight scene application of whole-system edge intelligent calculation container deployment can be realized, and a whole-system agile adaptive construction and adjustment edge intelligent hardware platform can be supported.

The invention belongs to the technical field of edge computing, and discloses an Internet of Things gateway collaborative management system based on edge computing, which comprises a resource virtualization unit used for deploying a protocol independence abstract engine in a gateway cluster, analyzing and adapting various Internet of Things protocols, constructing a proprietary protocol conversion matrix, obtaining standardized equipment attribute data, and storing the standardized equipment attribute data in a server; establishing an equipment shadow model based on the standardized equipment attribute data; the edge collaborative scheduling unit is used for outputting panoramic state data through the equipment shadow model, abstracting the gateway cluster into a tough topology directed graph based on the panoramic state data by adopting an edge computing task scheduling algorithm, and coordinating the gateway cluster; the intelligent load balancing unit is used for monitoring the gateway cluster calculation load and the network transmission load at the same time, and predicting the load imbalance rate according to the gateway cluster calculation load and the network transmission load; the efficient, flexible and intelligent cooperative management of the Internet of Things gateway cluster is realized.

The invention discloses a cluster computing resource allocation method. The method comprises the following steps: constructing a heterogeneous computing resource pool comprising an MT CPU, an FT CPU and an MTDSP cluster; an intelligent algorithm basic calculation library is deployed, hardware mapping of a deep learning algorithm is realized through an irregular matrix multiplication optimization module, and the irregular matrix multiplication optimization module comprises a microkernel automatic generation unit which dynamically generates an assembly instruction assembly line supporting three FMAC units according to matrix dimensions; the multi-core parallel strategy unit is used for providing M-dimensional and K-dimensional parallel calculation strategies and a dynamic scheduling mechanism; dynamically selecting a row block size parameter # imgabs0 # and a column block size parameter # imgabs1 # based on the matrix shape to carry out self-adaptive adjustment on the block size; physical resources are abstracted into containers or virtual machine instances through resource virtualization, and computing resources are dynamically allocated according to matrix dimension characteristics of the intelligent training module. According to the method, automatic resource deployment strategy generation and flexible computing resource scheduling are realized, and high-performance cluster computing resource allocation and deployment services are provided for users.

The invention discloses a heterogeneous AI computing power resource segmentation and scheduling method and system, and the method comprises the steps: recognizing the type of a heterogeneous AI computing power card accessed to a current server and hardware resource parameters of the heterogeneous AI computing power card, and carrying out the abstract modeling to generate a logic resource model; dividing a computing core and a video memory of a single heterogeneous AI computing power card into at least one independent virtual computing power instance through a resource virtualization technology on the basis of the constraint of a logic resource model; and in response to a computing task request, mounting the virtual computing power instance to a computing task in a containerization form, and dynamically scheduling the virtual computing power instance based on the demand of the computing task. Standardized logic resource units are generated through the minimum segmentation unit, and the utilization rate and scheduling flexibility of heterogeneous AI computing power resources are remarkably improved.

The invention provides a heterogeneous resource virtualization annotation and knowledge base creation method and system, and the method comprises the steps: obtaining a plurality of types of heterogeneous science and technology project resource sets, carrying out the cross-modal semantic feature extraction operation, and generating a cross-modal resource semantic feature set; constructing a semantic association network based on the cross-modal resource semantic feature set, performing a cross-modal virtualization mapping rule mining operation through the semantic association network to generate a cross-modal virtualization mapping rule set, and performing virtualization labeling processing on the heterogeneous science and technology project resource set through the cross-modal virtualization mapping rule set. Generating a virtualized annotation data set; and constructing an evolutionary heterogeneous resource knowledge base based on the virtualized annotation data set, wherein the evolutionary heterogeneous resource knowledge base comprises a concept unit, a relation unit and an attribute unit. By adopting the method provided by the invention, the organization, management and application level of heterogeneous resource knowledge can be effectively improved.

The invention provides a data processing method and a related device. The embodiment of the invention can be applied to a resource virtualization scene in the field of cloud computing. The method is applied to the cloud computing system, the cloud computing system comprises a service layer, a management layer, a virtualization layer and a resource layer, and the management layer is used for processing an identity label of a target virtual machine carried in a virtual machine access request after the virtual machine access request is obtained, obtaining a first character string, generating a target uniform locator according to the first character string, and sending the target uniform locator to the cloud computing system; when the second character string is the same as a preset character string, the target virtual machine is accessed through the target uniform resource locator, the second character string is a character string obtained by decoding the first character string, and the preset character string is a character string stored in the target virtual machine. The preset character string used for authentication in the target virtual machine does not need to be persisted to the database, so that the data security problem possibly caused by attack or leakage of the database is avoided, and the security of the virtual machine data is improved.

The invention discloses a switching communication device, a switching communication method and a server, relates to the technical field of computers, and realizes equipment-level decoupling and resource pooling basis by actively managing a GPU (Graphics Processing Unit) through a second interface configured to be in a root complex mode. The on-chip management system and the switching logic module form a control core, a data switching strategy is dynamically executed, and conversion from passive forwarding to autonomous management is achieved. And the virtual port module performs software definition and dynamic allocation on network resources, so that the flexibility of resource utilization is improved. Meanwhile, MAC ports in two modes of direct connection and exchange are supported, so that the device can flexibly construct a high-performance direct connection network or a large-scale exchange network, and the limitation of fixed topology is broken through. And the data processing module ensures high efficiency of data conversion between different interface protocols. According to the device, the communication efficiency and schedulability between the GPUs are improved by integrating high-speed exchange, intelligent management, resource virtualization and flexible networking capabilities.

The invention discloses a 1588 network card device, system, equipment and medium supporting synchronous clock resource virtualization, and belongs to the technical field of power system energy storage equipment, comprising clock synchronization, network card resource virtualization and network card clock virtualization. The clock synchronization comprises neighbor rate ratio, delay measurement, time offset and time convergence state offset; the network card resource virtualization comprises a physical function and a virtual function; the network card clock virtualization comprises a bottom-layer physical clock source, a virtual logic clock source, a PFTIMER and an upper-layer virtual timer, and has the beneficial effects that three core problems of multi-clock domain management, virtual machine clock isolation and high-precision timing in a multi-core heterogeneous system are solved through a hardware clock virtualization layered architecture; the complexity of system software is remarkably reduced, the real-time performance is improved, and a basic clock support is provided for a next-generation intelligent network card.

A method and device for implementing an IP network acquisition and control plug-in based on a Matrix command adapter, which relates to the field of network communication technology, includes parsing and converting control instructions of heterogeneous network devices through a Matrix command adapter to generate a standardized intermediate instruction set; deploying a resource virtualization pool in the IP network acquisition and control plug-in to dynamically allocate network bandwidth, computing resources, and device interfaces; and real-time matching the intermediate instruction set with the execution capabilities of target devices to generate executable device control instructions. Through a dynamic protocol adaptation layer, the present invention can flexibly parse and convert heterogeneous network device instructions, quickly adapt to new devices and new protocols through template matching and custom script extension, improve the compatibility and scalability of network management, and through a resource virtualization pool combined with a prediction model and a dynamic priority queue, intelligently allocate resources according to the real-time load and historical data of devices, improve resource utilization rate, ensure the operation of core business devices, and enhance the overall performance of the network.

A quantum hardware resource virtualization method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring a target quantum bit unit topology used for a target quantum computing task (310); acquiring a quantum bit unit network in a quantum hardware resource (320); in the quantum bit unit network, acquiring a plurality of candidate quantum bit unit sub-networks meeting the target quantum bit unit topology (330); on the basis of self-operation fidelity of quantum bit units in the candidate quantum bit unit sub-networks and mutual operation fidelity between adjacent quantum bit units, determining the overall operation fidelity of the candidate quantum bit unit sub-networks (340); and on the basis of the overall operation fidelity, determining a target quantum bit unit sub-network to serve as a virtualization resource used for the target quantum computing task (350). The method increases the utilization rate of a virtualization resource, and also ensures a relatively high operation accuracy. The method can be applied to various scenarios, such as quantum computing and superconducting quantum computing.

The application discloses a kind of switching communication device and method, server, it is related to computer technical field, by being configured as the second interface of root complex mode actively manages GPU, equipment level decoupling and resource poolization foundation are realized.The on-chip management system and switching logic module constitute control core, dynamically execute data exchange strategy, realize from passive forwarding to the change of autonomous management.Virtual port module carries out software definition and dynamic allocation to network resource, improve the flexibility of resource utilization.Meanwhile, support the MAC port of two modes of direct connection and switching, so that device can flexibly build high-performance direct connection network or large-scale switching network, break the limit of fixed topology.Data processing module then ensures the efficiency of data conversion between different interface protocols.The device improves the communication efficiency and schedulability between GPUs by integrating high-speed switching, intelligent management, resource virtualization and flexible networking capabilities.

A resource management mechanism oriented to distributed multi-quantum equipment comprises quantum computing resource virtualization, an independent QPU equipment topological structure in a distributed quantum computing environment and a connection relation between QPUs are abstracted into a graph data structure, and updating is carried out according to the real-time state of the quantum equipment. When real quantum computing resources in the system are insufficient, a super computer is used for simulating execution of a quantum circuit; a line scheduling mechanism in a distributed quantum computing environment determines whether a quantum line needs to be cut based on a quantum device state in a system and a resource demand of the quantum line to be executed. When distributed quantum computing needs to be executed, a popularity / weak connectivity mapping optimization algorithm is adopted, and the fidelity of a quantum program is improved. According to the invention, a brand new solution is provided for managing quantum computing resources in a distributed computing environment, the utilization rate of the quantum computing resources is improved, and the execution fidelity of a quantum circuit is improved.

The invention discloses an intelligent integrated disaster recovery method and system, and the method comprises the steps: accessing a device of an X86 architecture and a device of an ARM architecture, and calling a control interface corresponding to each device in a preset heterogeneous device driver library; accessing a computing resource Computeri, a storage resource Storagei and a network resource Networki of each piece of equipment i through the control interface, and constructing a heterogeneous resource pool (ResourcePool); the method comprises the following steps: virtualizing each resource in a resource pool into a standardized resource unit based on a software defined storage and software defined network technology; extracting attribute parameters for each resource unit j to form a resource identification tuple Rj corresponding to the resource unit j, and constructing a uniform resource abstract view table VRDL = [R1, R2, R3,..., Rj,...]; service data synchronization is carried out based on the constructed resources, the service load is monitored for each resource unit j, and the resource load index Wt at the time t is calculated; and when the Wt exceeds a preset threshold value Threshold, triggering a disaster recovery prompt and executing a disaster recovery switching operation by using the VRDL.

The present invention provides a resource pool architecture phased array system resource management device and method, belonging to the field of electronic equipment resource scheduling, comprising a beam resource pool management module for virtualizing the array surface resources of a spatial signal processing resource layer into a transmitting and receiving beam resource pool, a baseband resource pool management module for virtualizing the hardware processing resources of the baseband signal processing resource layer into a modulator resource pool and a demodulator resource pool, a computing resource pool management module for virtualizing the general computing and storage resources of an information data processing resource layer into a virtual machine resource pool, and a comprehensive operation management module for receiving a task plan, issuing a resource query command to the beam resource pool management module, the baseband resource pool management module, and the computing resource pool management module according to the task plan, sending a resource allocation application according to feedback, and establishing an interconnection matrix according to the link interconnection and backup requirements of the task plan, thereby realizing information interaction between multiple virtualized resource pools.

The invention relates to a resource virtualization processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that resource feature attributes of heterogeneous physical resources in a DPU recognition system are obtained, the types of the heterogeneous physical resources comprise computing resources, network resources and storage resources, and a uniform resource model is constructed based on the resource feature attributes; performing virtualization processing on heterogeneous physical resources of different computing examples in the uniform resource model, executing a preset resource mapping rule, mapping the heterogeneous physical resources of the different computing examples into a uniform virtual resource form, and obtaining a uniform virtualization layer; obtaining a resource allocation rule of various heterogeneous physical resources of different calculation instances in the unified virtualization layer; and performing resource allocation processing on the virtual hardware resources according to the resource allocation rule. By adopting the method, the accuracy of resource allocation can be considered while the real-time performance is ensured in a large-scale data center environment.

The application discloses an audio integrated network resource virtualization method, and belongs to the technical field of audio network resource management, and comprises the following steps: constructing a distributed virtual switching layer of a covering subnet, embedding a virtual switch instance into a kernel state network stack of each physical host in a cluster, and dynamically mapping cloud hosts in the subnet to the physical host group through a consistent hashing algorithm; when the cloud host sends an audio data packet, the source physical host virtual switch instance performs the following steps: extracting a DSCP field and marking it as EF, encapsulating through a GRE-over-UDP tunnel, and replacing a target MAC address with a virtual MAC address of a next-hop virtual switch instance; after the virtual switch instance of the target physical host is unpacked, the audio packet is delivered to the target cloud host based on a virtual port forwarding table. Through the distributed virtual switching layer and the consistent hashing mapping mechanism, the method realizes efficient virtualization transmission of audio data, supports QoS guarantee and low-delay transmission, and is suitable for audio network resource management in a large-scale cloud environment.