1712 results about "Workload" patented technology

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 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.

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.

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.

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.

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.

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.

Discussed herein are techniques that utilize hierarchical locality information of host machines included in a cluster network for the execution of general workloads. Hierarchical locality information for each host machine of a plurality of host machines is stored. The hierarchical locality information for a host machine identifying, for each locality of a plurality of localities, location information for the locality. Responsive to receiving a request requesting execution of a workload, the hierarchical locality information for the plurality of host machines is obtained and provided (e.g., to a customer) in response to the request.

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.

The embodiment of the invention provides a task processing device, a task processing method, electronic equipment and a storage medium. The task processing device comprises a calculation scheduling core particle and a function core particle which are integrated in a single package, the calculation scheduling core particle comprises a hardware subsystem and a calculation core, and the hardware subsystem is configured to receive and analyze task information to obtain at least one task. And assigning at least one task to at least one of the functional core and the computing core according to the task type; the computing core is configured to execute computing operations according to tasks allocated by the hardware subsystem, and the functional core particles comprise at least one of a quantum computing management core particle and a ray tracing core particle. According to the task processing device, through a unified task scheduling distribution mechanism, the adaptive capacity of the task processing device in an end-side mixed task scene is remarkably improved, so that a single computing chip can efficiently support various heterogeneous workloads.

The invention provides a training method of an index recommendation model and an index recommendation method and system.The training method comprises the steps that workloads and a candidate index set are obtained, and in the nth iteration, for each sub-network, recommendation indexes corresponding to the workloads are determined from the candidate index set so as to determine the local gradient of each sub-network, the index recommendation model comprises a plurality of sub-networks, updates parameters of the global network based on the local gradients corresponding to the sub-networks, and updates parameters corresponding to the sub-networks based on the updated parameters of the global network, the index recommendation model comprises the global network obtained through training convergence, and the index recommendation model is used for recommending index configuration for the to-be-recommended workloads. The training speed is greatly increased, and the stability and generalization ability of the index recommendation model can be improved.

The invention discloses a manufacturing method of an installation package and a related device, and relates to the technical field of software engineering. Comprising the steps that a target MakeFile is created, and other instruction items, except for an install instruction item and a unstall instruction item, in the target MakeFile are all empty; obtaining a target compressed package corresponding to the target software; based on the target MakeFile and the target compressed package, constructing an initial project; adding a management script of the target package in the initial project to obtain a target project; and based on the target project, generating a target installation package corresponding to the Kylin Linux operating system. The manufacturing method of the installation package does not depend on source code reconstruction, a large amount of workload for compiling and maintaining complete Makefile in a traditional scheme is omitted, the packaging period is greatly shortened, and meanwhile the compatibility and integrity of the installation package are guaranteed.

The present invention relates to the technical field of the Internet. Disclosed are a task-oriented dialog method, and a storage medium and a computer device. The method comprises: in response to receiving dialog information initiated by a user, a client sending the dialog information to a server; in response to receiving the dialog information, the server acquiring user information of the user, and on the basis of the dialog information and the user information, determining intent information of the user by means of a large model; on the basis of the user information and the intent information, the server generating a planning scheme by means of the large model, and executing execution steps in the planning scheme, so as to obtain a scheme execution result; the server sending the scheme execution result to the client; and the client receiving and displaying the scheme execution result. The method can effectively improve the accuracy of intent recognition and the accuracy of scheme planning, reduce the workload of scheme enumeration, and increase the degree of matching between a scheme execution result and a user intent, thus satisfying the actual requirements of users.

A system and method for streamlining and accelerating cloud migration processes may include a plurality of modules including an adaptive prompt module configured to create a library of prompts, maintain the library of prompts, and select one or more prompts from at least one large language model (LLM) based on a prompt strategy that may consider which prompts in the library are most likely to elicit accurate and relevant responses from the at least one LLM. Another module may perform inventory analysis to enhance the prompt strategy. A third module may perform workload transformations based on the library of prompts, and a fourth module may provide validation mechanisms. The adaptive prompt module also may include a self-learning module configured to enable integration across the plurality of modules.

The present application relates to the technical field of computers. Provided are methods and apparatuses for handling a non-recoverable system fault, and an electronic device and a non-transitory readable storage medium. A method is applied to a BMC, and comprises: collecting register data of a processor after a fault check signal is detected, wherein the fault check signal is sent to a BMC by an on-chip controller upon detecting that a flag bit of a fault isolation register is set; analyzing the register data to obtain positioning information of a faulty device; and triggering a warm reboot of a system, wherein during the warm reboot of the system, the positioning information is used for instructing a host BIOS to disable the corresponding faulty device on the basis of the positioning information. In the present application, after a non-recoverable system fault is detected, out-of-band collection and analysis are performed on the register data of the processor by means of the BMC, and the processes of data collection and analysis do not occupy processor resources, thereby greatly reducing the workload of the processor, ensuring the processing performance requirements of the processor for a real user service, and improving the stability of a server system.

The invention discloses a universal personnel fatigue degree and efficiency output comprehensive modeling method, which comprises the following steps of S1, performing initialization setting, and preparing input parameters; s2, establishing a judgment system to enter a working stage, and continuously describing a dynamic evolution trend of fatigue gradually accumulated along with superposition of time and workload based on a fatigue evolution comprehensive model; s3, nonlinear efficiency mapping is calculated based on the fatigue degree F (t) and a work output calculation model, and the work efficiency of the person in the current state is represented; s4, determining the adjusting efficiency of the circadian rhythm; s5, calculating a task output integral based on efficiency; s6, performing personnel fatigue degree and efficiency output stage modeling through a rest stage efficiency recovery model when it is obtained based on system identification that the personnel are in a non-working state or a rest stage; wherein the rest stage efficiency recovery model is a Sigmoid model; and S7, comprehensive modeling is completed through time advance and stage switching. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

Devices, methods, and systems for shutdown and restart management in a distributed computing environment are described herein. One method includes determining whether a backup of a workload in a distributed computing environment exists, causing a shutdown sequence of the workload to occur at a first predetermined time to shut down the workload, and causing a restart sequence of the workload to occur to restart the workload in the distributed computing environment.

A power consumption prediction and management method and system based on a solid state disk, a medium and a product relate to the field of solid state disks. The method comprises the following steps: predicting the workload intensity of a host in a first preset time window based on an I / O (Input / Output) instruction stream to obtain an expected host workload curve, and determining an expected host idle time period; calculating an expected host power consumption value in the expected host idle time period; extracting internal state parameters of the solid state disk according to the historical operation log data; establishing a background task triggering probability model, and calculating the triggering probability of starting the high-power-consumption background task by the solid state disk in a first preset time window; performing power consumption conflict judgment according to the expected host power consumption value and the triggering probability, and generating a comprehensive predicted power consumption value; and based on the comprehensive predicted power consumption value, determining an optimal target power consumption state, and instructing the solid state disk to be switched to the optimal target power consumption state. By implementing the technical scheme provided by the invention, the stability of state switching of the solid state disk is improved.

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.

Aspects of the disclosure are directed to dynamic workload partitioning. In accordance with one aspect, the disclosure includes a first system on a chip (SOC) configured to commence a use case execution using a baseline workload partition; and a controller coupled to the first SOC, the controller configured to determine if the baseline workload partition should be reallocated using a machine learning (ML) monitoring of system on a chip (SOC) temperatures.