1924 results about "Workload" patented technology

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

An illustrative method includes identifying, based on static workload data associated with a compute environment, one or more attack paths from a network to one or more datasets associated with an entity, accessing runtime workload data associated with the compute environment, and performing, based on the runtime workload data, a risk mitigation operation associated with the one or more attack paths.

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

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses a large model cue word intelligent design and verification system supporting multi-person cooperation, which designs cue words in a manner of cue word project management and multi-person cooperation to realize specialized design of the cue words. The difficulty of cue word design is simplified by using AI aided design; the operation stability and accuracy of cue words in different models and different computing power are verified through single-point testing and batch testing, so that cue word verification and iteration form a closed loop, and the cue word quality is improved; after the cue word is published, directly outputting an interface with an access key for external calling, and storing the interface as a cue word case into a cue word knowledge base; compared with an existing system and technology, the cue word version management difficulty is effectively reduced, the workload of cue word designers is reduced, the cue word quality is improved, and the risk of cue word asset loss is reduced.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

A method for managing a workload deployment includes: receiving a request; obtaining metadata and a policy associated with an edge node (EN); analyzing, against the policy, the request and the metadata to infer a current state (CS) of the EN; making, based on the analyzing, a determination that the CS of the EN is healthy and a workload associated with the request is suitable for the EN; and sending, based on the determination, a response to a scheduler to indicate that the scheduler is allowed to deploy the workload to the EN.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is configured to detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

The disclosed computing device can include collection circuitry configured to collect current resource utilization per shared resource location. The computing device can also include formulation circuitry configured, using expected resource utilization information of a new workload and the current resource utilization, to provide a scheduler with one or more expected utilization metrics. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods are provided for determining expert placement layouts and / or prompt and toke routing for a mixture of experts (MoE) model. In some instances, an expert workload distribution with respect to a plurality of experts is determined based on a gating neural network. In some instances, an expert placement layout with respect to a plurality of computing units is determined based on the determined expert workload distribution. In some instances, a two-level routing strategy is provided to first adaptively route an incoming prompt to a suitable computing device, then perform a token routing within that computing device to ensure workload balance between different computing units of that computing device.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to indicate one or more computing resources to be used by one or more instructions based, at least in part, one or more characteristics of that software workload.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is presented. The system is configured to: detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

The invention discloses a server hardware testing terminal and method, and relates to the technical field of server hardware testing, and the method comprises the steps: obtaining server hardware configuration parameters and storage equipment specification information, collecting the number of processor cores, memory capacity and storage equipment read-write speed reference data through a system monitoring interface, building a hardware performance baseline file, and storing the baseline file in a server; obtaining a complete hardware resource list and a performance index range; the method comprises the following steps: constructing a multi-dimensional workload model according to information retrieval scene features, creating query request sets of different scales by adopting a random number generator, simulating a mixed load mode of full-text retrieval and database query, and determining resource consumption weights and execution time distribution of various query operations; and analyzing query request distribution characteristics in the workload model through a dynamic load balancing algorithm, if query requests are concentrated in a specific time period, adjusting a load distribution strategy, obtaining a uniformly distributed test load sequence, and judging the impact degree of a load peak value on hardware resources.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

In some aspects, a cloud-based computer system includes: a quantum computing system comprising a quantum processing unit; a container management and execution system configured to receive a container and execute a program within the container; and a communication channel between the container management and execution system and the quantum computing system for providing program instructions to the quantum computing system. The container management and execution system and the quantum computer system may be co-located in a data center or located in different data centers. The latency of the communication channel may be selected to optimize cost for a required computer performance.

The invention provides a safety measure generation method and system based on a power distribution network multi-task parallel work ticket, and the method comprises the steps: receiving a to-be-executed work ticket containing a plurality of parallel tasks, and extracting the task information of each task; obtaining a plurality of task groups and task group associated equipment sets corresponding to the task groups; obtaining a task group minimum operation sequence set; obtaining a task group topology boundary equipment set, and performing topology search by using a topology boundary equipment set subset to obtain a safety measure set of each task group; and based on the minimum range maintenance decision model, optimizing the safety measure set of each task group to obtain a final multi-task parallel safety measure set. According to the scheme, the tasks which are electrically connected adjacently are combined into one task group, so that repeated operation on the same equipment can be avoided, unnecessary workload is reduced, and the safety measure generation efficiency of the safety measure generation method based on the multi-task parallel work ticket of the power distribution network is improved.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.

The invention relates to the technical field of AGV robot scheduling and control, and discloses an AGV robot scheduling method and system of an intelligent factory and a storage medium. The method comprises the steps of collecting bearing sensing data to identify a product height value according to a product carrying instruction and a transportation route, and if the value is higher than a reference value, generating an auxiliary scheduling instruction; next, nearby non-loaded AGVs are searched to serve as temporary robots, scheduling values are obtained in combination with task load and distance sorting of the temporary robots, and the optimal robot is selected to serve as a slave robot to be paired with the original AGV robot to jointly bear products; and finally, the master and slave robots respectively respond to the matched scheduling instructions. According to the method, through master-slave cooperation and accurate scheduling, the AGV transportation stability and efficiency are improved.

The invention discloses an intelligent processing method for automatic settlement of review labor reward of a review expert, and belongs to the technical field of business data processing, and the method comprises the steps: obtaining text revision trajectory data and cooperative interaction data of the review expert, and generating a review behavior feature vector; performing recessive workload analysis on the review behavior feature vector to generate a quality weight factor; obtaining project environment parameters, and calculating to obtain a reward amount according to the project environment parameters, the quality weight factor and a preset settlement coefficient; performing exception verification on the reward amount, if the verification is passed, executing payment operation, and otherwise, triggering a manual rechecking process; and updating the settlement coefficient according to the feedback data after payment. According to the method, the reward is dynamically calculated by quantitatively reviewing the behavior characteristics and fusing the environmental parameters, and an exception verification and closed-loop optimization mechanism is established, so that fair and efficient full-process automatic settlement can be realized.

Provided herein are techniques to facilitate providing consumer-defined workloads for a network slice of a slice provider network. In at least one example, a method may include transmitting a query to a slice provider function for topology information of an instantiated network slice in which the instantiated network slice includes slice resources provided at a plurality of network locations. The method may further include obtaining the topology information that identifies, at least in part, the plurality of network locations at which the slice resources and identifying, based at least in part on the topology information, at least one network location of the instantiated network slice at which to instantiate a consumer-defined workload, and transmitting a request to instantiate the consumer-defined workload that includes at least one location identifier of the at least one network location and a workload identifier of the consumer-defined workload to be instantiated.

A method for detecting password spray attacks. The method includes obtaining information from an on-machine malware detection application for a particular machine indicating that a password spray tool is detected on the particular machine. Information is obtained indicating that the particular machine has performed failed sign in attempts. As a result, a determination is made that the particular machine is performing password spray attacks.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.

An application workload is performed on data by a plurality of processor devices, where the data is stored in a first memory associated with a first one of the processor devices and a second memory is associated with a second one of the processor devices. Accesses of the data from the first memory by the plurality of processor devices are monitored. The data is transformed from a first form to a second form based on the accesses, and the data is transformed in the second form to the second memory based on the accesses.

The present disclosure relates to systems and methods for flow scheduling of machine learning workloads. The systems and methods use prior knowledge of the traffic patterns for machine learning workloads and the network topology to schedule the flows of the machine learning workloads. A centralized controller leverages the knowledge of the traffic patterns for the machine learning workloads to guide per-flow routing decisions for the machine learning workflows to schedule the flows across all available network paths to fully utilize network bandwidth capacity.

A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources. The system enables intelligent consolidation and efficient server management within a datacenter environment.

The invention relates to the technical field of cloud computing, and provides a cloud native assembly type application construction method and device supporting dynamic combination. According to the method, through component standardized packaging and distributed warehouse management, the high component reuse rate is achieved, the repeated development workload is reduced, a visual arrangement tool and a process template are reused, the application construction period is shortened by 50% or above, business personnel can directly participate in development, the'business-technology 'communication cost is reduced, and the development efficiency is improved. And during dynamic combination, an optimal loading path is calculated by adopting an optimization particle swarm algorithm of global search of a particle swarm algorithm and local kick of a simulated annealing algorithm, so that intelligent dependency analysis and optimal path calculation are realized, and the complexity of manual dependency sorting is avoided.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent file editing and auditing system and method and a storage medium, and the method comprises the following steps: receiving a purchase file; vectorization processing is carried out on the purchase file; newly establishing a purchase document; relevant data are recalled from the vectorized data according to target information required by the chapters, structured conversion is carried out, and the data are added into the corresponding chapters, so that compilation of the purchase document is completed; the compiled purchase document is preprocessed; the method comprises the following steps: reading review requirements from a preset document review point library, extracting related review contents from vectorized data of a purchase document, performing review judgment on the related review contents according to the review requirements by utilizing a large language model, and outputting a review judgment result, thereby completing intelligent review of the purchase document. According to the method, manual content retrieval, extraction, integration, filling and auditing work can be replaced, the workload and time are greatly saved, and meanwhile the accuracy and speed of information retrieval are obviously improved compared with manual information retrieval.