435 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.

Method and apparatus for protecting computer resources from malicious attack including baseline sentinels and warrior sentinels. Baseline sentinels are deployed on a network serving only as decoys and containing no company data. When any attempt to communicate with a baseline sentinel is detected, a host of warrior sentinels (also containing no company data) are deployed to act as additional decoys, diminishing the chance that a malicious attack will reach a valuable computer resource and collecting information on the malicious attacker. Once the malicious attack stops or is defeated, the warrior sentinels are retired and the system resets to baseline sentinels.

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 discloses a multi-tenant management method and system based on containers and Kubernetes, and relates to the technical field of computer resource management.Tasks of tenants are packaged into a plurality of container instances, the container instances are submitted to a Kubernetes cluster for calculation, each container instance is subjected to Gang scheduling in the Kubernetes cluster, GPU resources of an AI platform are subjected to critical analysis, and when a critical edge is reached, the Kubernetes cluster is subjected to Gang scheduling in the Kubernetes cluster. All the tenants are classified according to the payment conditions of the tenants, scheduling priority ranking is carried out on all categories in combination with cluster center values, and dynamic allocation and management strategy generation are carried out on GPU resources of each tenant in each category by using a dynamic scheduling algorithm. A distributed deployment architecture is adopted, computing power resource management is performed through a containerization technology and a Kubernetes cluster, GPU resource allocation is performed by using a dynamic scheduling algorithm, and the management efficiency of GPU resources is effectively 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.

The invention provides a query method and device based on multiple data sources, a medium and electronic equipment, and relates to the field of data processing.The query method comprises the steps that in response to a received target query request of a user, a target request server corresponding to the target query request of the user is obtained; determining at least one key executable route from a preset basic route library according to the target query request; obtaining at least one data source to be updated according to each key executable route; controlling the target request server to obtain query data corresponding to the target query request according to the authority of each to-be-updated data source for a data interface set by the target request server; according to the method, the number of checking times is reduced, the checking and ordering ratio is controlled to be within a reasonable range, occupation of computer resources is reduced, the checking time is saved, and the experience feeling of a user is improved.

The invention discloses a resource intelligent joint optimization method based on deep reinforcement learning, and the method comprises the following steps: 1, building a communication network topology, and detecting the data flow in the network topology; 2, randomly generating a task flow, and simulating analysis and sending of tasks in the network topology; 3, the switch receives the task and searches n simple paths of the task; 4, setting constraint conditions, and screening candidate paths from the n simple paths; step 5, taking the minimum total time delay as a target, selecting an optimal path from the candidate paths, and distributing optimal computer resources and communication resources for the optimal path; and step 6, issuing the communication resources allocated to each link of the optimal path to a switch to realize communication resource allocation. According to the method provided by the invention, more concurrent tasks can be effectively accommodated on the premise of ensuring the task delay requirement, so that the resource utilization rate is maximized.

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.

The embodiment of the invention discloses an evaluation method of a machine learning model, a data processing method and related equipment, the method can be used in the field of automatic driving in artificial intelligence, and the method comprises the following steps: processing a plurality of fragment data in an evaluation sample through the machine learning model to obtain a plurality of prediction tags, determining a parameter value of at least one evaluation index; the evaluation sample comprises description data of the traffic scene in a first duration, the fragment data comprises description data of the traffic scene in a first sub-duration, and the first duration comprises the first sub-duration; the at least one evaluation index indicates stability and / or accuracy corresponding to the plurality of prediction labels, and the accuracy is obtained based on true values corresponding to the plurality of fragment data; the performance of the machine learning model applied to the vehicle driving process can be reflected more accurately by constructing the evaluation index on the granularity of the traffic scene, the simulated traffic scene does not need to be established, the consumed computer resources are reduced, and the evaluation efficiency is improved.

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.

Aspects of the present disclosure relate to receiving a request from a remote device for access to an online computer-based service, transmitting to the remote device a request for a verifiable token encoding a verifiable credential containing an issuer's digital signature signed with a private key and an identifier indicating that a holder of the verifiable credential is human. If the verifiable token was not received, access to the online computer-based service is inhibited. If the verifiable token is received, its validity is confirmed utilizing an associated public key. If the validity is confirmed, the request from the remote device for access to the online computer-based service is determined to be from a human and the remote device is enabled to access the online computer-based service.

The invention discloses a task scheduling method and device, equipment, a medium and a product. The method comprises the steps that in the process that a heterogeneous parallel system obtains tasks, if it is detected that a new task arrives, whether task scheduling conditions are met or not is determined according to the number of arrived tasks and the initial time when the first task arrives; if yes, determining a candidate scheduling sequence corresponding to the to-be-allocated task, and determining candidate completion time required for completing execution of the to-be-allocated task under each candidate task scheduling sequence according to a preset task scheduling information table; and determining a target task scheduling sequence according to the candidate completion time corresponding to each candidate task scheduling sequence, so as to distribute the to-be-distributed task to a corresponding target execution server to complete task scheduling. According to the technical scheme, the batch tasks can be distributed and scheduled in a unified mode, the task scheduling efficiency of the heterogeneous parallel system is effectively improved, and computer resources are saved.

The invention relates to a distributed photovoltaic computer computing power control method and system, and the method comprises the steps: obtaining photovoltaic power generation output power data and computer CPU (Central Processing Unit) computing power demand data, and carrying out the preprocessing; establishing a correlation model of photovoltaic power generation output power data and computer CPU computing power demand; constructing a function of the computer CPU utilization rate and the computer CPU power consumption; according to the correlation model of the photovoltaic power generation output power data and the computer CPU computing power demand, the computer CPU utilization rate and the computer CPU power consumption function, the computer CPU computing power is regulated and controlled; and optimizing the correlation model of the photovoltaic power generation output power data and the computer CPU computing power demand according to the photovoltaic power generation output power data, the computer CPU power consumption and the computer CPU resource use condition in the operation process. The invention provides a distributed photovoltaic computing power control method and system, which can utilize new energy to the maximum extent and reduce the energy consumption of a computing system.

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.

The embodiment of the invention provides a computer resource processing method, and relates to the field of electric digital data processing, and the computer resource processing method comprises the steps: obtaining a directed acyclic graph of a target workflow; determining a dependency relationship of each node in the directed acyclic graph, the dependency relationship including a dependency resource of each node, and the dependency resource including a model; in the execution process of the target workflow, obtaining a dependency resource of an executed node in the directed acyclic graph as a target dependency resource; determining whether the target dependency resource is depended by an unexecuted node of the directed acyclic graph or not based on the dependency relationship; and releasing the target dependency resource under the condition that the target dependency resource is not depended by the unexecuted nodes of the directed acyclic graph. According to the technical scheme, the problem of video memory overflow in the multi-model cooperation process can be effectively avoided, meanwhile, the workflow execution time can be reasonably shortened, and the system efficiency is improved.

A method provides a set of computer data statistical profiles derived from a corresponding set of samples of computer data to a ransomware detection system and obtains a prediction of the likelihood of a ransomware attack in the set of samples of computer data. The system utilizes a machine learning system trained to achieve data models, with each model trained initially on a corresponding cluster of curated computer data statistics profiles, each cluster including statistics characterizing a corresponding cluster of curated samples resulting from exposing a selection of raw data samples to processing by actual ransomware. Each model is subject to iterations against initial validation data until performance convergences, with sample sources from the same backups not being present in both training and validation models. The models have been subject to final validation against actual customer data to address data drift that would otherwise result in excessive false predictions.

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.

A method for authorizing access to one or more secured computer resources includes obfuscating a reference biometric vector into an obfuscated reference biometric vector using a similarity-preserving obfuscation. An authentication biometric vector is obfuscated into an obfuscated authentication biometric vector using the similarity-preserving obfuscation. A similarity of the obfuscated authentication biometric vector and the obfuscated reference biometric vector is tested. Based on the similarity being within an authentication threshold, access to the one or more secured computer resources is authorized.