73 results about "Resource specification" patented technology

The invention relates to a task scheduling method and device, computer equipment, a storage medium and a program product. The method comprises the steps of determining priorities of tasks to be executed in at least two task queues according to a preset time period, and updating the at least two task queues according to the priorities; according to the updated task sequence of the at least two task queues, the resource demand of each to-be-executed task and the resource specification of each node in the node pool corresponding to the at least two task queues, allocating each to-be-executed task to a matched node for processing; wherein the type of the node pool comprises a reasoning type and a training type, and each task queue corresponds to one type of node pool. By adopting the method, the priority of the to-be-executed tasks in the task queue can be periodically updated, the situation that the to-be-executed tasks with different priorities cannot be scheduled is avoided, meanwhile, the to-be-executed tasks are allocated to more appropriate nodes to be processed, the task processing efficiency can be improved, and the task fine scheduling effect can be optimized.

The disclosure provides a method for assigning containerized workloads to isolated network constructs within a networking environment associated with a container-based cluster. The method generally includes receiving, at the container-based cluster, a subnet port custom resource specification to initiate creation of a subnet port object to assign a node to a subnet within the networking environment, wherein one or more containerized workloads are running on the node, in response to receiving the subnet port custom resource specification, creating the subnet port object, and modifying a state of the container-based cluster to match a first intended state of the container-based cluster at least specified in the subnet port object, wherein modifying the state comprises assigning the node to the subnet in the networking environment.

The invention discloses an inference service resource allocation method, electronic equipment and a readable storage medium, and relates to the technical field of computers, the inference service resource allocation method comprises the following steps: obtaining a model file of an inference service, and analyzing the model file to determine a minimum storage resource demand required for running the inference service; determining a resource specification meeting the minimum storage resource requirement, wherein the resource specification is used for representing nodes required for running the reasoning service and storage resource configuration corresponding to the nodes; and according to the storage resource state of the node in the reasoning cluster, determining the resource configuration strategy matched with the resource specification, and configuring the storage resource in the reasoning cluster according to the resource configuration strategy to operate the reasoning service, thereby solving the technical problems of low automation degree and poor utilization rate of the storage resource when resource configuration is performed on the reasoning service, and improving the resource configuration efficiency. The error risk and cost of manual operation are reduced, and the utilization rate of storage resources is also improved.

The invention is applicable to the technical field of cloud computing resource management and scheduling, and provides a resource scheduling method, device, equipment and medium based on instance specification awareness, and the method comprises the following steps: collecting multi-dimensional resource information and task load information of a plurality of computing nodes in a cloud computing platform in real time, and according to the multi-dimensional resource information and the task load information, scheduling the computing nodes according to the multi-dimensional resource information and the task load information; a target scheduling action meeting the instance specification requirement at the current moment is obtained through a pre-trained multi-dimensional resource specification perception scheduling model, the multi-dimensional resource specification perception scheduling model is obtained through population-based reinforcement learning training mechanism training, and task resource allocation is executed according to the target scheduling action. And the task execution result is monitored, and the multi-dimensional resource specification perception scheduling model is adaptively updated based on the feedback information obtained by monitoring, so that the cloud environment resource utilization efficiency, the task matching rate and the dynamic environment response speed are improved.

Systems and methods collect input information about a function to offload, a representation of the code, application requirements, user preferences, resource profiles, and capabilities and apply a cost function to weigh these factors as applied to available offloading and local processing options. The cost function outputs a list of resource specifications that fulfill the application requirements and user preferences together with information about expected performance, energy consumption, monetary cost, and other aspects of the potential offloading. Application developer tools assemble input data and evaluate candidates for offloading functionality, which are used in static, semi-static, and dynamic deployments. They are also applied in a control plane of a framework for dynamic offloading to field requests and evaluate whether potential offloading solutions are beneficial.

The present invention relates to a resource object (26) operated in a modular processing system (10) for execution of at least one task (28) in support of modular processing system operation. Every task (28) is defined by a requested capability specification representing an expected functionality for task execution and / or by a requested resource specification representing at least one processing resource expected for task execution. As tasks describe capabilities and / or resources to be provided for related task execution the resource object (26) can execute autonomous process resource configuration. The present invention also relates to a processing resource pool providing processing resources for use through the resource object (26) during autonomous process resource configuration.

The disclosure provides a method for assigning containerized workloads to isolated network constructs within a networking environment (100) associated with a container-based cluster. The method generally includes receiving, at the container-based cluster, a subnet port custom resource specification to initiate creation of a subnet port object to assign a node to a subnet within the networking environment (100), wherein one or more containerized workloads are running on the node, in response to receiving the subnet port custom resource specification, creating the subnet port object, and modifying a state of the container-based cluster to match a first intended state of the container-based cluster at least specified in the subnet port object, wherein modifying the state comprises assigning the node to the subnet in the networking environment (100).

Embodiments of the present application provide a resource reservation method and a resource allocation method, a device, a storage medium and a program product. In the embodiments of the present application, cloud resources reserved for multiple clients are unified into a reserved resource pool, and the reserved resource pool is shared by the multiple clients, so that the binding relationship between the cloud resources and the clients can be eliminated, the reserved cloud resources in the reserved resource pool can be reused by the multiple clients, the utilization rate of the cloud resources is improved, and the resource selling rate of a cloud vendor is improved. In the resource reservation, the overall use condition information of the current cloud resource reservation is reasonably predicted by learning the historical information related to the cloud resource reservation of the multiple clients, and the overall resource reservation quantity of each resource specification is determined in combination with the resource specification and the resource quantity of the cloud resources reserved by the multiple clients, so that the reserved resource quantity can be saved, and the resource reservation cost is reduced.

This application relates to the field of artificial intelligence technology, and in particular to a computing power resource scheduling method and apparatus. This method can overcome the bottleneck of computing power resources of a single physical machine. In response to a first user instruction to create a function, the method displays first resource specification information; the first resource specification information describes the first resource specification corresponding to the user-created main function; in response to a second user instruction to enable a slave function, the method displays second resource specification information; the second resource specification information describes the second resource specification corresponding to at least one slave function attached to the main function; the total computing power obtained based on the first and second resource specifications is greater than the computing power limit corresponding to a single physical machine; the first and second resource specification information are uploaded to a server; the server is used to obtain a first container image corresponding to the main function and at least one second container image corresponding to at least one slave function; a cloud service program is deployed in the first container image.

The technology disclosed describes a Quantum Utility Exchange (QUE) framework for managing and trading Quantum Utility Exchange Traded Resources (QU-ETRs). Providers define quantum resource packages (with time windows, usage limits, and resource specifications) and list them in a Recorded Database. Entities granted usage rights can trade these packages on the QUE through buy, sell, swap, or bid transactions, with all ownership transfers recorded and expired / consumed packages delisted. A Pending Database supports offers to buy or sell, searchable with AI / ML tools. The exchange uses secure blockchain-based communications, encrypted private tunnels, and verification processes to ensure integrity. Interfaces for providers and consumers enable package submission, searching, bidding, and downloading through a networked computer system. QU-ETRs can be traded under flexible contract types (“buy now-use now / later” or swaps), with transaction, validation, and alert mechanisms built into the system. Overall, the method establishes a secure, structured marketplace for allocating and exchanging quantum computing resources.

The invention provides a service resource determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining a target resource type and a to-be-estimated energy measurement parameter, determining a target energy measurement reference value matched with the to-be-estimated energy measurement parameter in a preset reference scheme list, calculating a proportional relation between the to-be-estimated energy measurement parameter and the target energy measurement reference value, and determining a service resource according to the proportional relation. Determining resource specification configuration information corresponding to the target energy reference value, and determining target resource specification configuration information in a preset configuration sorting list corresponding to the target resource type based on the proportional relation and the resource specification configuration information corresponding to the target energy reference value; according to the method, the corresponding resource specification configuration information is dynamically calculated by matching the energy quantity parameter with the reference value in the benchmark scheme list in combination with the proportional relation and the configuration sorting list, so that inaccurate resource allocation caused by empirical judgment is avoided, the accuracy of the resource specification configuration information is effectively improved, and the user experience is improved. And the phenomenon of insufficient resource support or redundant resource waste caused by inaccurate resource allocation is reduced.

The application relates to a resource intelligent allocation method and device of a virtual desktop based on multi-dimensional constraints, which comprises the following steps: identifying the CPU performance type of a blade server, dynamically adjusting the super-division coefficient based on the CPU performance type of the blade server; calculating the total core number of a virtual CPU, calculating the theoretical allocation number of a virtual machine based on the total core number of the virtual CPU, combining the virtual desktop specification demand of a user, the physical resource of the blade and the number of the allocated virtual machines of the blade; calculating the actual allocation number of the virtual machine based on the total core number of the virtual CPU and the upper limit of the physical resource specification of the blade; judging whether the allocation result meets the multi-dimensional constraints based on the theoretical allocation number of the virtual machine and the actual allocation number of the virtual machine; when the allocation result meets the multi-dimensional constraints, establishing an allocation priority rule, selecting an optimal target blade based on the allocation priority rule, executing the virtual machine resource allocation by using the optimal target blade, and updating the virtual machine resource state.

The embodiment of the invention provides a server screening method and device based on multiple screening conditions, a storage medium and a program product. In a server screening method, a plurality of parallel screening conditions are divided into an environmental dependency condition and at least one resource specification condition. Wherein the cloud servers in the first cloud server set can be sorted according to the priority attribute of the environment dependence condition, and a second cloud server set is obtained. And then, according to the sequence of the cloud servers in the second cloud server set, screening out a target cloud server matched with the at least one resource specification condition from the second cloud server set. The cloud servers are sequenced based on the priority attributes of the environment dependence conditions, so that the cloud servers meeting the environment dependence conditions can be preferentially subjected to specification matching, the target cloud server can be screened out by traversing the second server set once, the traversing times during cloud server screening are greatly reduced, and the screening efficiency is improved. And the screening efficiency of the cloud server is improved.

The invention provides a data processing method. The method comprises the steps of obtaining a basic load of a target application; splitting the basic load into at least one minimum execution unit; according to the resource specification required by the at least one minimum execution unit, determining a target resource instance corresponding to the at least one minimum execution unit from a target resource pool, and executing the at least one minimum execution unit by the target resource instance, the target resource pool comprising a plurality of pooled resource instances, and the resource specification required by the at least one minimum execution unit; the plurality of pooled resource instances are obtained by virtualizing physical resources, and the physical resources are arranged in at least one cloud data center managed by the cloud management platform. According to the scheme, waste of computing resources can be avoided, and the execution speed of the application is increased.

The method comprises: generating a new firewall instance virtual machine according to new system resource specification information; obtaining an original firewall instance virtual machine to be updated; calling a configuration lock to add the configuration lock to data of the original firewall instance virtual machine; obtaining the data of the original firewall instance virtual machine, importing the data into the new firewall instance virtual machine, and leading traffic to the new firewall instance virtual machine through a virtual gateway; releasing the configuration lock, and closing and recycling the original firewall instance virtual machine. In the method, the data of the original firewall instance virtual machine is obtained, and the data is imported into the new firewall instance virtual machine, so that the update of the firewall instance capacity can be implemented in the security protection of the firewall without closing the original firewall instance virtual machine and affecting existing production services.

The current document is directed to methods and systems that generate recommendations for resource specifications used in virtual-machine-hosting requests. When distributed applications are submitted to distributed-computer-system-based hosting platforms for hosting, the hosting requester generally specifies the computational resources that will need to be provisioned for each virtual machine included in a set of virtual machines that correspond to the distributed application, such as the processor bandwidth, memory size, local and remote networking bandwidths, and data-storage capacity needed for supporting execution of each virtual machine. In many cases, the hosting platform reserves the specified computational resources and accordingly charges for them. However, in many cases, the specified computational resources significantly exceed the computational resources actually needed for hosting the distributed application. The currently disclosed methods and systems employ machine learning to provide accurate estimates of the computational resources for the VMs of a distributed application.

The embodiment of the invention provides a resource adjustment method and device and a storage medium. In the embodiment of the invention, through pre-binding the corresponding relationship between the software specification information of the target software and the underlying resource specification, when the target software is adjusted, the software can be adjusted to the version corresponding to the target specification information according to the target specification information of the target software; in addition, according to the corresponding relation between the pre-bound software specification information of the target software and the underlying resource specification, the target resource specification matched with the target specification information can be obtained, the resources used by the target software are adjusted to the target resource specification, and integrated adjustment of the software and the underlying resources on which the software depends is achieved.

The invention discloses a cloud computing resource intelligent configuration and automatic opening method and system based on a large language model. The method comprises the following steps: receiving and preprocessing an original demand described by a user in a natural language, and generating standardized information; inputting the data into a large language model for deep understanding and intention recognition, and automatically generating an optimal configuration scheme comprising specific resource specifications, deployment positions and load balancing strategies; according to a load balancing strategy dynamically decided by a large language model in the scheme, the resources are optimally distributed in combination with real-time resource load data; the scheme is analyzed and converted into a specific API calling sequence for different cloud service providers through a multi-cloud hybrid management system, and resource opening and deployment are automatically completed. According to the method, end-to-end intelligent processing from natural language requirements to multi-cloud resource automatic configuration is realized, and the configuration efficiency, the resource utilization rate and the agility of cross-cloud management are remarkably improved.

Various embodiments of the present technology generally relate to systems and methods for providing a service model engine. In an aspect a method includes identifying, by a service model engine, a swagger file from a network vendor and parsing the swagger file for API endpoints. The service model engine may generate resource specifications based on the API endpoints and may generate delivery actions for each API endpoint. Each delivery action may include delivery parameters for each API endpoint, The service model engine may also map each of the delivery parameters to corresponding characteristics in the resource specifications to generate parameter mappings. The service model engine may generate a service delivery model including the resource specifications, the delivery actions, and the parameter mappings, where the service delivery model is used by a service orchestration system to deliver respective services.

The invention provides a resource allocation method of a virtual cipher machine and a cloud server, and relates to the technical field of information security. The method comprises the following steps: acquiring a current resource specification grade of a target virtual cipher machine, and a processing resource quota and an encryption card occupation unit corresponding to the current resource specification grade; according to the processing resource quota, distributing a corresponding target processing resource for the target virtual cipher machine from the processing core of the physical cipher machine; according to the encryption card occupation unit, allocating a corresponding target memory resource to the target virtual cipher machine from the memory resources of the physical cipher machine; and according to the encryption card occupation unit, distributing a corresponding target password card and a target encryption channel on the target password card for the target virtual cipher machine from a plurality of password cards inserted into the physical cipher machine. The problem of low resource utilization rate in resource allocation of the virtual cipher machine is solved, and the resource use efficiency of the cipher service in the cloud environment is improved.

The application provides a model production method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to a data access request, accessing training data from a data source and processing the training data to obtain a target data set; in response to a model access request, selecting a target model matched with the access request from a model warehouse, wherein the model access request carries model type information and model accuracy information; in response to a resource scheduling request, determining a target resource through resource specification information, wherein the resource specification information is determined based on the target data set and the target model; and in response to a model training request, executing a training task based on the target resource, wherein the training task is created based on the target data set and the target model. The application can decouple each stage of model production, each stage can complete its own function, and the scalability, robustness, reusability and portability of model production are improved.

The invention relates to the technical field of GPU resource allocation, in particular to a GPU resource dynamic allocation and cross-node migration method and system based on a containerization environment, and the method comprises the steps: monitoring a container state corresponding to a container in real time, and collecting dimension data corresponding to each dimension in a corresponding data collection dimension set; evaluating the GPU resource use condition corresponding to the container at the current moment, and generating corresponding GPU resource evaluation result data; judging whether the container needs to perform data migration at the current moment or not; if yes, identifying and capturing an incremental data layer generated when the container runs; pre-deploying the same basic mirror image layer on the target node, and configuring a required GPU resource specification; quickly migrating the captured incremental data layer to a target node; and starting a new container on the target node based on the basic mirror image layer and the migrated incremental data layer.

The embodiment of the present application provides a control method, device, equipment and system for an application instance, the method comprising: determining that all application instances of a target application need to be released, the current application instance of the target application being a first application instance; controlling the creation of a second application instance of the target application and releasing the first application instance, so as to control the second application instance to respond to a network request of the target application, the resource specification of the second application instance being different from the resource specification of the first application instance. The present application avoids the problem of slow response to network requests caused by recreating application instances upon receiving a network request of an application, shortens the response time of network requests, and realizes timely response to network requests.

This invention relates to the field of GPU resource allocation technology, specifically to a method and system for dynamic allocation and cross-node migration of GPU resources in a containerized environment. The method includes: real-time monitoring of the container status corresponding to the container; collecting dimensional data corresponding to each dimension in a data collection dimensional set; evaluating the GPU resource usage of the container at the current moment and generating corresponding GPU resource evaluation result data; determining whether the container needs data migration at the current moment; if so, identifying and capturing the incremental data layer generated during container runtime; pre-deploying the same base image layer on the target node and configuring the required GPU resource specifications; quickly migrating the captured incremental data layer to the target node; and starting a new container on the target node based on the base image layer and the migrated incremental data layer.

The invention relates to a cloud host hybrid deployment method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring the total resource of each physical node and the resource specification of a cloud host; the cloud host comprises a first virtual machine and a second virtual machine; determining each initial combination scheme of each physical node based on the total resource of each physical node, the resource specification of the first virtual machine and the resource specification of the second virtual machine; for each initial combination scheme of each physical node, calculating a comprehensive distribution rate corresponding to the initial combination scheme, and determining a target combination scheme of each physical node based on each comprehensive distribution rate; and deploying the first virtual machine and the second virtual machine at the corresponding physical nodes according to the target combination scheme. By adopting the method, the resource utilization rate of the physical nodes can be improved.

The invention discloses a resource programmable elastic acceleration method based on YARN, and relates to the field of big data storage computing, the method comprises a user, a resource management master node, a resource management slave node and an application program coordinator, the method provides a dynamic resource application and life cycle management method, and resource idleness and waste are reduced. Through a resource validity period matching mechanism, task resource recovery failure is avoided; a request ReSource interface is provided, a client is allowed to dynamically apply for resources through a YARN API, and resource specifications and use time windows can be flexibly configured; the applied dynamic resources are bound to a specified queue, only tasks of the queue can be used, and the scheduler is preferentially scheduled to a dynamic resource node; after the resources expire, calling a release interface to release the resources, and updating the queue quota; according to the method, the problem of rigidity of YARN static resource allocation is effectively solved, elastic scheduling and dynamic management of resources are realized, and the method is suitable for high-concurrency and high-fluctuation complex service scenes.

The current document is directed to methods and systems that generate recommendations for resource specifications used in virtual-machine-hosting requests. When distributed applications are submitted to distributed-computer-system-based hosting platforms for hosting, the hosting requestor generally specifies the computational resources that will need to be provisioned for each virtual machine included in a set of virtual machines that correspond to the distributed application, such as the processor bandwidth, memory size, local and remote networking bandwidths, and data-storage capacity needed for supporting execution of each virtual machine. In many cases, the hosting platform reserves the specified computational resources and accordingly charges for them. However, in many cases, the specified computational resources significantly exceed the computational resources actually needed for hosting the distributed application. The currently disclosed methods and systems employ machine learning to provide accurate estimates of the computational resources for the VMs of a distributed application.

The application discloses a reasoning service resource configuration method, an electronic device and a readable storage medium, relates to the technical field of computers, and comprises the following steps: obtaining a model file of a reasoning service, analyzing the model file to determine minimum storage resource requirements required for running the reasoning service; determining a resource specification that meets the minimum storage resource requirements, wherein the resource specification is used to represent nodes required for running the reasoning service and storage resource configurations corresponding to the nodes; determining a resource configuration strategy matched with the resource specification according to storage resource states of the nodes in a reasoning cluster, and configuring storage resources in the reasoning cluster according to the resource configuration strategy to run the reasoning service, thereby solving the technical problems of low automation and poor utilization of storage resources when resources are configured for the reasoning service, reducing the error risk and cost of manual operation, and improving the utilization of storage resources.

The invention relates to the technical field of computing power scheduling, in particular to a computing power resource scheduling method based on GPU virtualization, electronic equipment and a storage medium, and the method comprises the following steps: an application end initiates a computing power application through a computing power portal, and the application at least comprises a computing power scheduling task type and a required resource specification; analyzing the application, obtaining a task type and a resource specification, and setting a priority category for the to-be-scheduled instance; according to an analysis result, scheduling the task to a data center physical resource node according to a priority category through a container cloud platform scheduler, and creating a corresponding instance to execute the task; virtual GPU resources are provided for the instance, and the virtual GPU resources are obtained by virtually segmenting physical GPU resources. According to the invention, three core technologies of GPU virtualization, container and virtual machine mixed arrangement and priority scheduling are fused, an efficient, flexible and intelligent computing power resource scheduling system is constructed, and the problems of low GPU resource utilization rate, single scheduling mode, insufficient task priority guarantee and the like in the prior art are solved.

The present application provides a method, system and storage medium for rust detection based on edge computing, which relates to the field of computer vision technology. The method includes: using an enhanced shuffle network to perform lightweight improvements on the backbone network of the target detection model, using a bolt data set to train the lightweight target detection model, and then obtaining a lightweight model that adapts to the edge computing hardware platform through format conversion and quantization; using a number of cores that matches the hardware resource specifications in the edge computing hardware platform, and performing collaborative reasoning on the lightweight model based on real-time acquired bolt images to quickly obtain real-time rust detection results. By performing targeted optimization on the architecture and deployment method of the rust detection model, the speed and performance of the model deployed in the edge computing device can be effectively improved, and real-time robust bolt rust detection can be achieved based on edge computing.