316 results about "Computer resources" patented technology

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention discloses an intelligent resource scheduling system and method based on an elastic threshold value and AI prediction, and relates to the technical field of computer resource management. For the limitation of the existing resource scheduling method, the provided scheme comprises an elastic threshold configuration module used for defining performance indexes of computing resources, allocating weights and setting initial upper and lower limits of an elastic threshold; the dynamic threshold value generation module is used for collecting and calculating resource performance index data in real time, calculating a real-time dynamic threshold value after preprocessing, and comparing the real-time dynamic threshold value with an elastic threshold value range; the AI prediction module is used for training and optimizing a prediction model, and the model obtains a future computing resource demand prediction result based on the new data; and the real-time scheduling engine module is used for formulating a resource scheduling strategy, distributing computing resources, executing computing resource increasing and decreasing operation, monitoring a scheduling result and feeding back the scheduling result, so that the modules are adjusted, and a dynamic optimization closed loop of the resource scheduling strategy is formed. The method is used for improving the computing resource utilization rate.

The invention relates to a heterogeneous GPU cluster scheduling optimization method and system based on resource affinity, and belongs to the field of computer resource scheduling. Comprising the steps of 1, according to an affinity historical library of historical jobs, matching the historical jobs with relatively high similarity through job characteristics, and estimating resource requirements; 2, for a new job, receiving the job, performing probe testing on each type of GPU, and obtaining and recording job performance data; 3, establishing an affinity evaluation model for each job by using historical information or test data, evaluating affinity values of the jobs under different resource configurations, and providing data support for resource allocation decisions; and 4, entering a scheduling optimization period cycle, constructing an allocation matrix according to a target function of an optimization problem, and allocating corresponding resources to each job. According to the method, accurate resource matching and data support are achieved, the dynamic adaptive capacity is guaranteed, and the system expandability is enhanced.

Sanitizing data can be a cumbersome task, particularly when the volume of data is large, the content is sensitive, and / or the type of sanitation requires contextual determinations. Sanitizing large amounts of data is tedious and may often require highly trained personnel with clearances and / or other qualifications. In the systems and methods of the present disclosure, language models (LMs) are used to solve these and other technical issues with tools that may allow sanitizing data easily, with high versatility, context awareness, and / or low demand for computational resources. In particular, some of the disclosed systems and methods use a first language model and a second language model (being less resource-intensive than the first language model) to generate sanitized output data with improved efficiency and accuracy. This dual-model approach ensures that sensitive information is handled appropriately while optimizing computer resource usage.

The invention discloses a processor, a graphics card, equipment and a resource allocation method and device, and belongs to the technical field of computer resource management. The processor comprises a thread group assembling unit, a thread group scheduling execution unit and at least two processing units, the thread group assembling unit is used for assembling the tasks into one or more thread groups based on the attribute information of the tasks in a task assembling stage; and allocating computing resources for one or more thread groups with the thread group granularity, wherein each thread group is allocated with a processing unit; the thread group scheduling execution unit is used for scheduling one or more thread groups to the processing unit corresponding to each thread group for execution according to a unified scheduling rule, and the unified scheduling rule is a scheduling rule shared by different types of tasks; and the at least two processing units are used for executing the scheduled thread groups. And the computing resources are allocated according to the thread group granularity, so that the execution efficiency of the processor is improved.

The invention discloses a computer resource allocation management system and method based on data analysis, and relates to the technical field of computer resource management.The method comprises the steps that data are collected in real time, and historical data are collected; constructing a spatio-temporal data warehouse based on the real-time data and the historical data; extracting static features and dynamic features of the task, and combining the static features and the dynamic features to generate a high-dimensional task feature vector; constructing a resource semantic knowledge graph, and defining a computing resource entity, a relationship and a constraint rule; combining the spatio-temporal data warehouse and the resource semantic knowledge graph to construct a demand prediction model, and outputting probability distribution prediction of future resource demands; and in combination with real-time data, calculating resource use efficiency and a load balancing index in real time, and dynamically adjusting a resource allocation decision. The demand prediction model is constructed based on the spatio-temporal data warehouse and the resource semantic knowledge graph, resource allocation is carried out actively, resource shortage or idle is avoided, and the resource utilization rate is improved.

The invention relates to the technical field of cloud computer resource utilization, in particular to a method and a system for reserving and scheduling network transmission resources in a credential cloud computer, which comprise a real-time network sensing technology, a dynamic resource reserving mechanism and a centralized scheduling strategy. The method has the advantages that the resource reservation proportion is dynamically adjusted by sensing the network state and task requirements in real time, global optimal allocation is achieved through the centralized scheduler, and therefore the network resource utilization rate is increased, and the service quality is guaranteed; through a dynamic resource reservation mechanism and a scheduling strategy, maximum utilization of cloud computer resources is realized, user experience in use of the cloud computer is improved, special network requirements of different services are met, the method is suitable for low-delay and high-concurrency network transmission requirements in high-reliability scenes such as finance and government affairs, the service quality is guaranteed, and the method is suitable for large-scale popularization and application. And the performance and the reliability of the cloud computer system can be obviously improved.

Systems and methods related to a variable self-executing program. A variable self-executing program comprises a self-executing program that is minted with an indication of a non-transferable ownership wallet (e.g., a digital wallet for which the self-executing program is minted), a non-transferable network address that has permissions for updating metadata for the variable self-executing program, and an off-chain profile state based on a specific user corresponding to the digital wallet.

Systems and methods for a multi-camera object detector having two or more backbone models. In particular, systems and methods are provided for including two or more backbone machine learning models, with one backbone optimized for speed and the other backbone optimized for precision. In particular, the first backbone can be highly accurate but slower than the second backbone and with a higher computer resource usage. The second backbone can be fast and efficient but have lower accuracy for object detection. In some examples, the second backbone can use fewer images and / or lower resolution images. The determination of which backbone to use can be based on fixed rules, or it can be determined based on another machine learning component. The outputs from the first and second backbones for each camera can be combined together into a unified representation, such as a bird's eye view (BEV) space.

This invention discloses a full lifecycle management system and method for scientific computing programs. The system includes a build environment subsystem and a production environment subsystem. The former provides computer resources for the build process of the scientific computing program throughout its lifecycle, while the latter provides computer resources for the testing and deployment processes. This invention, through a full lifecycle management method for scientific computing programs, operates on corresponding computing resources, encompassing a series of steps including querying, triggering scheduling, build execution, result distribution, and test deployment. It automatically generates the executable file of the scientific computing program and configures its runtime dependencies. Simultaneously, it automatically generates a corresponding description file recording the entire lifecycle process. Furthermore, based on version management of these description files and their sets, it achieves full lifecycle traceability and cross-platform migration and deployment of scientific computing programs in a high-performance computing environment.

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.

Computer-implemented methods for recommendation prioritization for a container orchestration system. Aspects include receiving a set of recommendations for a cluster of a container orchestration system. Aspects also include selecting an optimal recommendation from the set of recommendations using a scored knowledge transform graph. Aspects further include generating a confidence score for the cluster based on the optimal recommendation. Aspects also include determining a category of a readiness assessment model for the cluster using the confidence score. Aspects further include modifying a computer resource of the cluster based on the category of the readiness assessment model.

A method of causing ransomware to be executed includes obtaining a set of ransomware samples and automatically performing a set of scripted detonations of a first subset of the ransomware samples according to a schedule. The set of scripted detonations includes executing the first subset of ransomware samples on a virtual machine. During the execution of the first subset of ransomware samples, use of computing resources by the first subset of ransomware samples is monitored until a shutdown threshold is reached. Upon reaching the shutdown threshold, any pending detonation tasks of the execution of the first subset of ransomware samples are purged. Detonation-infected disk images are generated to include files resulting from the execution of the first subset of ransomware samples, and the detonation-infected disk images are used in training a machine learning system to assess a likelihood of a presence of a ransomware attack.

The invention discloses a game playing method, device and equipment based on a virtual scene, a medium and a program product, and relates to the field of virtual scenes. The method comprises the following steps: displaying a first virtual character and n second virtual characters belonging to a first camp in a virtual game scene; in response to the first virtual character and at least one virtual character in the n second virtual characters meeting a foothold occupation requirement, displaying a foothold occupation animation; and under the condition that the first camp obtains the foothold occupation permission, updating and displaying the game data of the first camp. In the application, the virtual character automatically controlled by the system is introduced into the virtual game scene in the point occupation mode, and the virtual character can replace or enter the stronghold in the virtual scene together with the virtual character controlled by the player, so that the risk brought by entering the stronghold is shared for the player, the enthusiasm of the player to enter the stronghold to participate in competition is improved, and the user experience is improved. Therefore, the utilization rate of the resources related to the strongholds in the virtual office is improved, and waste of computer resources is avoided.

The invention discloses an RAG-oriented embedded service flexible deployment method, and belongs to the technical field of computer resource configuration. The method comprises the following steps: firstly, obtaining an embedding model suitable for an RAG scene, constructing an embedding service, testing different resource configuration parameters in batches through automatic resource analysis, and screening out optimal resource configuration meeting service quality requirements; secondly, an RPS monitoring and dynamic batch processing mechanism is integrated, and a GPU resource fragmentation optimization algorithm and a three-level candidate GPU scheduling strategy are combined; triggering a hybrid capacity expansion and contraction mechanism according to a reasoning request load to form cooperative scheduling; and finally, reasoning requests are distributed through a polling mechanism, and service performance is continuously optimized. Through the synergistic effect of automatic resource analysis, GPU resource fragmentation optimization and hybrid capacity expansion and contraction strategies, the GPU resource utilization rate and embedded service performance can be remarkably improved, and the high-concurrency and low-delay reasoning requirements in an RAG scene are effectively met.

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 discloses a probabilistic route map path planning method and device. The method comprises the following steps: S1, acquiring global position information and obstacle information of an automatic driving vehicle; s2, sampling to obtain candidate path nodes; s3, constructing a covariance matrix; s4, calculating the mahalanobis distance between each candidate path node and the barrier distribution; s5, discarding sampling points whose mahalanobis distances are smaller than a set safety threshold, and performing preliminary screening on candidate path nodes; s6, constructing a probability route map based on the screened candidate path nodes; and S7, completing path planning. According to the method, useless and low-efficiency sampling nodes can be accurately eliminated, so that the construction of the probabilistic route map is simpler and more efficient, and the construction quality and effect of the probabilistic route map are greatly improved; meanwhile, interference and calculation burden caused by invalid nodes are reduced, and the efficiency of path planning is remarkably improved; in addition, waste of computer resources is reduced, and resource utilization efficiency is improved.

Sanitizing data can be a cumbersome task, particularly when the volume of data is large, the content is sensitive, and / or the type of sanitation requires contextual determinations. Sanitizing large amounts of data is tedious and may often require highly trained personnel with clearances and / or other qualifications. In the systems and methods of the present disclosure, language models (LMs) are used to solve these and other technical issues with tools that may allow sanitizing data easily, with high versatility, context awareness, and / or low demand for computational resources. In particular, some of the disclosed systems and methods use a first language model and a second language model (being less resource-intensive than the first language model) to generate sanitized output data with improved efficiency and accuracy. This dual-model approach ensures that sensitive information is handled appropriately while optimizing computer resource usage.

A request associated with access to a restricted computer resource by a computer application of a device is received via a first communication medium. It is determined that the request is provided by the device with an IP address not included in a group of authorized IP addresses. A registration secret is generated. A representation associated with the registration secret is provided via a second communication medium. A token signed using the registration secret is received. In response to successfully validating the token, a communication secret is generated and associated with an identifier associated with the device. The communication secret is provided for use by the computer application of the device to access the restricted computer resource.

The invention discloses an enterprise supervision method based on a cloud management platform and related equipment, and relates to the field of computers. The method comprises the steps that when an employee user logs in a cloud computer account, a work task plan of current login is acquired from the employee user, and the work task plan comprises at least one task type; based on the work task plan, evaluating cloud computer resource configuration data required by the employee user for logging in this time; and selecting a matched core time mode based on the cloud computer resource configuration data, wherein different core time modes have different rates. The problem that a charging mode of a cloud computer is generally based on calculation configuration and use time and is huge in expenditure for enterprises with large demands can be solved.

The invention relates to the technical field of data processing, and particularly discloses a data optimization system for economic management, which comprises a computer resource demand prediction module, a resource allocation benefit evaluation module and a resource allocation optimization module. The demand prediction module is used for predicting future resource demands by collecting performance index data in real time in combination with an ARIMA model and an LSTM network; the resource configuration benefit evaluation module arranges resource configuration and economic benefit data, establishes an economic benefit function, and represents a nonlinear relationship between resource configuration and benefit; and the resource allocation optimization module globally optimizes the economic benefit function by adopting a genetic algorithm, and finds an optimal resource allocation strategy. Through dynamic evaluation and optimization, excessive or insufficient allocation of resources is avoided, a local optimal solution is avoided by using a genetic algorithm, and economic benefit maximization is realized.

The invention discloses an online resource allocation method and system based on multiplicative weight updating, and belongs to the technical field of computer resource scheduling. In order to solve the problems that the resource utilization rate is low, the service quality is difficult to guarantee and the feedback information is limited in a multi-user sharing scene, a multiplicative weight updating strategy based on a binary queue state is mainly adopted, and the resource allocation proportion is dynamically adjusted in combination with a truncated simplex projection mechanism. According to the method, collaborative optimization of resource utilization efficiency maximization and service quality guarantee can be realized under the condition of only depending on the empty / non-empty state of the queue, and the method has good efficiency, robustness and fairness.

The invention provides a path display method and device in a game, electronic equipment and a storage medium. Providing a graphical user interface through the terminal device, displaying at least part of the game scene and an interactive building located in the game scene by the graphical user interface, and displaying a floor map of a specified floor in response to the virtual character entering the building; in response to the path recording function starting operation, recording a moving path of the virtual character on each floor; and displaying the moving track in the floor map based on the moving path. Therefore, by automatically recording and visualizing the moving track, the cognitive load of a player in a complex building is remarkably reduced, the resource searching efficiency is improved, the game experience is improved, and meanwhile computer resource consumption caused by repeated exploration is reduced.

The invention discloses a graphics card multi-task co-processing system based on dynamic video memory allocation, which belongs to the technical field of computer resource management and comprises a task grading module, a video memory state scanning module, an allocation strategy module and a resource recovery integration module. The task grading module is used for acquiring tasks, dividing the tasks into high-priority tasks and low-priority tasks, and establishing an independent resource pool according to initial priorities of the tasks; the video memory state scanning module is used for scanning a video memory use state in the independent resource pool and generating a video memory resource distribution map; the allocation strategy module is used for generating an allocation instruction based on the video memory resource distribution graph and the task priority; and the resource recycling and integrating module is used for recycling the continuous blocks released after the high-priority task is finished and updating the video memory resource distribution map. Through the task grading module, the video memory state scanning module, the allocation strategy module and the resource recovery integration module, the system response efficiency is improved.

The invention discloses a test and evaluation method for a virtualization system in multiple scenes, which comprises the following steps: deploying an executable file compiled by a test script at a virtualization client, and testing the performance of a client operating system to judge the performance loss of the virtualization system to computer resources. The method comprises the following steps: step 1, creating a project, and configuring system parameters; 2, integrating a test program into a project, and generating a mirror image file; step 3, packaging a complete virtualization system mirror image; 4, transmitting the mirror image to a development board through a network protocol; 5, executing a command to start a system, and logging in a system console through a serial port; and 6, calling and executing the function test script, the performance test script and the security test script.

The present application relates to the technical field of computers, and discloses a data processing system and method, a device and a nonvolatile readable storage medium. In the present application, each CPU core group of a disk array is bound with a physical network interface card, a physical hard disk drive, and a physical memory which are exclusive and would not be found by other CPU core groups, thereby implementing the isolation and division of physical network interface card resources, physical hard disk drive resources and physical memory resources in the disk array; by means of a network device, any host establishes, with the exclusive physical network interface card of a single CPU core group among a plurality of CPU core groups, an exclusive communication link which would not be found by other hosts and other CPU core groups; therefore, a CPU core group (i.e., a die) and a host communicate with each other by means of an exclusive communication link, so that the host accesses the CPU core group and the bound physical hard disk drive and physical memory, without using cross-die access, thereby shortening access paths, saving computer resources, and improving access performance and access efficiency.

The invention relates to an instruction jump prediction method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a to-be-processed instruction obtained by decoding, and acquiring structure behavior information corresponding to a conditional jump instruction under the condition that the to-be-processed instruction is identified as the conditional jump instruction; performing feature extraction based on the structural behavior information to obtain structural features; obtaining a sensor corresponding to the condition jump instruction; and inputting the structural features into the sensor, and performing jump prediction to obtain a predicted jump result. By adopting the method, the instruction jump prediction accuracy can be improved, so that the instruction execution conflict is reduced, and the waste of computer resources is reduced.

The invention provides a display method and device, electronic equipment and a computer medium, and the method comprises the steps: displaying a preset scene picture which comprises a plurality of objects; in response to detection of a moving operation for a first object in the multiple objects, multiple pieces of target bounding box information corresponding to the multiple objects are obtained, and each piece of target bounding box information in the multiple pieces of target bounding box information is used for representing a parameter of an axis alignment bounding box corresponding to the object corresponding to the target bounding box information; based on the multiple pieces of target bounding box information, determining a to-be-adsorbed object corresponding to the first object in the multiple objects; and based on the target distance information between the first object and the to-be-adsorbed object, determining whether to display an adsorption effect of adsorbing the first object to the to-be-adsorbed object. According to the technical scheme provided by the embodiment of the invention, the effects of improving the calculation efficiency and saving computer resources can be achieved.

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.

A generative model, e.g. a large language model (LLM), may be accessed by users over a network. A user might experience latency in the response from the generative model. To address the technical problem of latency, in some embodiments, the latency of the response from a first generative model is measured. If the latency falls within a particular range, then a switch to a second generative model is performed. In some embodiments, if the first generative model is not yet finished providing the response, then the partially-completed response from the first generative model is not deleted. Instead, the second generative model provides the remaining portion of the response so that the switch appears transparent and seamless to the user, and does not require restarting the generation process, thereby avoiding or mitigating the loss of already generated output and hence saving computer resources.