568 results about "Lower priority" patented technology

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention relates to the technical field of electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention discloses a multi-queue scheduling method based on total and incremental reasoning overhead merging, which comprises the following steps of: firstly, calculating the total resource demand and memory overhead of a task according to the input length and the size of a predictor model in the process of performing total reasoning on a reasoning task; distributing tasks to queues with different priorities according to the total resource demand in combination with response time requirements and task importance; dynamically scheduling tasks according to a priority queue sequence, adjusting an execution sequence in combination with a system load, and preferentially executing tasks in a high-priority queue; distributing a time slice for each task, monitoring task execution time, reducing the priority of overtime tasks, moving the overtime tasks into a secondary priority queue, and triggering new task scheduling; and after preemptive scheduling, the key value cache of the latest scheduling task in the waiting queue is swapped from the accelerator memory to the CPU memory, and is swapped back to the accelerator memory before the task is re-executed.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

The invention discloses a bidirectional feedback-driven large-model dynamic memory regulation and control system and method, and the system comprises a router, a generator, an actuator and a memory system, and is optimized through four core mechanisms: 1) a dynamic grading mechanism: dividing memory grades based on timeliness, accuracy, repeatability and context consistency by using fuzzy comprehensive evaluation; 2) a forgetting mechanism: performing knowledge distillation compression on the low-priority memory by adopting TinyBERT, and clearing redundant data; 3) an enhancement mechanism: combining similar task memories through LLM to generate an optimized narrative text; and 4) extracting a mechanism, and accurately retrieving knowledge in combination with a potential semantic index and retrieval enhancement generation technology, realizing closed-loop regulation and control of task allocation, generation, execution and memory enhancement through bidirectional interaction of actuator feedback and a memory system, remarkably improving storage efficiency, task solving speed and long-term self-adaptive ability, and greatly improving the system performance. The method is suitable for complex scene optimization of a large language model.

The invention discloses an automatic equipment scheduling method and system for an electromagnetic compatibility test, and the method comprises the steps: monitoring the state and spatial position information of each piece of equipment in real time, and calculating the electromagnetic radiation intensity of the equipment; sorting all the test tasks according to the priorities, and sequentially arranging the equipment from high priority to low priority for testing to form an initial equipment scheduling scheme; calculating the electromagnetic interference gradient of the equipment scheduling scheme, dynamically adjusting the equipment scheduling sequence by adopting the electromagnetic interference gradient, and updating the equipment scheduling scheme; the electromagnetic interference intensity between the devices is updated in real time, and then a device state interference matrix is dynamically evolved; global coupling feedback is calculated; and solving a multi-dimensional optimization objective function which comprehensively considers the total interference variable, the equipment state interference matrix and global coupling feedback under the current scheduling scheme to obtain an optimal equipment scheduling scheme. The method is high in scheduling efficiency, full in resource utilization, accurate in electromagnetic interference control, and capable of effectively meeting complex multi-device collaborative test requirements.

The invention discloses a federated learning terminal selection and resource scheduling method based on a dynamic priority, and relates to the technical field of user selection and bandwidth allocation of federated learning, and the method comprises the steps: obtaining a model basic structure and resource information parameters exchanged between a federated learning central controller and a user; calculating a priority value of each user according to the resource information parameters, and sorting the users according to the priority values from large to small; selecting a user subset meeting a training delay requirement based on a user priority sequence; for the user subset, calculating the minimum bandwidth allocation proportion required for completing the training task, and calculating and considering the data volume, the calculation capability and the network condition of the user; bandwidth resources are allocated to the user subsets by adopting a dichotomy, and bandwidth allocation is adjusted through iteration by adopting the dichotomy; and judging whether the total energy loss of the user subset exceeds a given energy constraint threshold, and if so, deleting the user with the lowest priority from the user subset. The federal learning performance is improved to the maximum extent.

The invention relates to the technical field of graphics processors, and provides a dynamic graphics processor sharing method, which comprises the following steps of: receiving submission of a plurality of deep learning tasks; generating a dynamic priority queue containing high-priority tasks and low-priority tasks; transmitting a resource state to the low-priority task through a broadcast mechanism, and triggering resource adjustment according to a comparison result of the real-time thread block usage amount of the high-priority task and a preset threshold value; according to the difference value between the real-time thread block usage amount of the high-priority task and a preset threshold value, the number of executable thread blocks of the low-priority task is dynamically adjusted; checking whether the number of thread blocks required by the kernel is smaller than or equal to the number of executable thread blocks of the current low-priority task, if yes, starting execution, and if not, reserving the kernel in a kernel request queue to wait for next cycle scheduling; and periodically updating the dynamic priority queue until all deep learning tasks are completed. According to the technical scheme, the overall efficiency of the system can be improved, and resource waste is reduced.

The invention provides an NPU instruction level context switching method and device, and relates to the technical field of data processing.The method comprises the steps that when a context switching instruction is received, a non-refresh instruction is forbidden, it is waited for completion of an instruction being executed, and context data is stored in a main storage module; after completion, recovering the context switching related instruction of the NPU module, and executing the target task; and after the target task is executed, recovering the context data to the NPU module, and recovering the execution of the non-refresh instruction. According to the method, optimization is carried out aiming at the problem that an existing NPU architecture generally can only support limited preemption at an algorithm boundary or a specific context switching point and even cannot preemption, so that a high-priority task must wait for a low-priority task to be completed, and remarkable priority inversion and / or key inversion are / is caused. A full-stack solution from a system on chip to an instruction set architecture and then to an operating system kernel is provided, and preemption of an NPU instruction level is achieved.

The invention provides a task scheduling method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the following steps: adjusting the current priority of each task to be scheduled in a main queue according to the current resource load; under the condition that the priority adjustment frequency corresponding to the to-be-scheduled task reaches a preset frequency and the duration of the to-be-scheduled task entering the main queue exceeds a first preset duration, the to-be-scheduled task is stored in a retention queue as a retention task; for the detention tasks with overlong waiting time in the detention queue, the detention tasks are added into the main queue again after the priorities are adjusted, so that the tasks are scheduled to be dequeued and executed according to the dynamically adjusted priority sequence, the tasks with high priorities can be executed as soon as possible, and the situation that the tasks with low priorities cannot be processed for a long time can be avoided; the efficiency and the fairness of task processing are considered under the condition of high concurrency.

A method including generating a queue of a plurality of workloads for execution in a data processing environment, where the plurality of workloads are organized in the queue based on respective priority values, and where a vulnerability scan is queued with a lower priority value and segmenting the vulnerability scan using at least one selected from a group consisting of: parallel segmentation and iterative segmentation. The parallel segmentation is configured to implement respective segments of the vulnerability scan at predetermined times to reduce downtime or performance impact of the data processing environment and the iterative segmentation is configured to repeatedly segment the vulnerability scan until respective segments of the vulnerability scan can be implemented in the data processing environment without any downtime or significant performance impact. The method further includes implementing the segmented vulnerability scan in the data processing environment.

The invention provides a multi-channel arbiter, which comprises a basic arbiter and a polling arbiter, and is characterized in that the basic arbiter is responsible for judging whether high-priority application signals exist in application signals or not, and arbitrating the high-priority signals in sequence until all the high-priority application signals are completed; during the period, the polling arbiter does not work; after the high-priority arbitration is completed, the polling arbitrator performs polling arbitration on the low-priority application signal, and the execution sequence is determined by a loop pointer; finally, arbitration results of the basic arbiter and the polling arbiter are output into an arbitration result signal through a first gate. According to the invention, efficient and ordered task scheduling can be realized.

The invention discloses a server fault processing method and device and a medium, and relates to the technical field of computers. And the server fault analysis problem is converted to obtain vector information, and the vector information is input to the pre-training model for reasoning analysis, so that accurate fault positioning is realized, and the search accuracy of fault analysis is improved. The fault analysis problem comprises a potential fault analysis problem, so that unknown faults are predicted and reasoned, and the fault processing delay is reduced. In inference analysis of a pre-training model, corresponding target inference strategies are matched based on priorities of different fault modes and association degrees of multiple faults instead of being directly processed one by one according to the priorities of the faults, and the association degrees of the multiple faults and whether the corresponding inference strategies are conflicted or not are concerned firstly; according to the method, the situation that secondary faults are caused by contradictory occurrence of heavy faults under the condition that the faults with low priorities are processed in a delayed mode is avoided, the fault maintenance progress is further accelerated, and the reliability of the server is improved.

The invention discloses a priority scheduling method and system for an on-orbit data processing task. The method comprises the following steps: constructing a DAG model, decomposing a macroscopic task into subtasks, defining a subtask dependency relationship, and endowing a subtask node with a priority attribute; setting or dynamically adjusting the priorities of the sub-tasks according to task service types, processing delay requirements or external instructions; maintaining a schedulable queue containing subtasks which have completed precursor tasks and meet scheduling conditions; calculating task scheduling priority indexes based on the queue, sorting, and selecting optimal calculation resource allocation; the priority is dynamically adjusted according to task waiting time or an external instruction during execution, and hunger of low-priority tasks is avoided; the multiple processing units execute the high-priority tasks in parallel according to needs; and after the task is completed, updating a subsequent node dependency state, and adding a new schedulable task cycle. According to the method, high-priority task resource allocation can be guaranteed preferentially, low-priority task hunger is avoided, and the overall throughput rate of the system and the self-adaptive capacity to on-orbit diversified tasks are remarkably improved.

A user equipment maps a high priority hybrid automatic repeat request-acknowledgment / negative acknowledgment (HARQ-ACK / NACK) information to one or more resource elements (REs), and maps one or more of a low priority HARQ-ACK / NACK information, channel state information (CSI), or uplink-shared channel (UL-SCH) data to REs while rate matching around the mapped high priority HARQ-ACK / NACK REs. A network entity receives an uplink transmission, including high and low priority HARQ-ACK / NACK information, CSI, and UL-SCH data mapped to high and low priority HARQ-ACK / NACK REs, CSI REs, and UL-SCH REs, respectively. The low priority HARQ-ACK / NACK REs, the CSI REs, and the UL-SCH REs are rate matched around the high priority HARQ-ACK / N ACK REs. The network entity demaps and decodes the REs of the high and low priority HARQ-ACK / NACK, the CSI, and UL-SCH to obtain the high and low priority HARQ-ACK / NACK information, CSI, and UL-SCH data.

The invention discloses a CAN message priority dynamic arbitration method and system based on event triggering, and the method comprises the steps: obtaining a discrete sampling signal of a CAN node of an engineering vehicle and a state register value of a CAN controller, converting the discrete sampling signal into a working condition data sequence, and obtaining a bus load rate through the state register value; obtaining an event state sequence according to the working condition data sequence; all CAN messages to be sent are constructed into a message data record, and the message data record with event semantics and dynamic priority labels is obtained according to the protocol data unit and the event state sequence; and according to the bus load rate and the event state sequence, sending a message data record with event semantics and dynamic priority annotations by adopting a token bucket mechanism through a three-level priority queue linked with a three-state state machine. According to the invention, the security key message in the system can still be transmitted in time under high load, and the bandwidth occupation and delay jitter caused by the low-priority message are avoided at the same time.

Examples described herein relate to circuitry to: monitor utilization data for a plurality of processes; determine one or more priority levels associated with at least one of the plurality of processes based on policy parameters; and adjust a frequency of operation of the interface circuitry based on the monitored utilization data and the determined priority levels of the processes. In some examples, adjust the frequency of operation of the interface circuitry is to prioritize frequency of operation requested by a higher priority workload over a frequency of operations requested by a lower priority workload.

The invention provides a computing resource scheduling method, device and system based on bare metal or containers, and the method comprises the steps: predicting first computing power resource data through a pre-trained safety water level prediction model, and obtaining an inventory safety water level corresponding to a current time period; performing system initialization deployment, monitoring use data of target computing power resources and system resources in real time, and converting the use data into inventory information; when a newly added user resource creation request is monitored, analyzing to obtain a target priority label; if the target priority label is a high-priority label and the ratio of the number of idle groups to the number of total groups is judged to be not greater than a preset threshold value corresponding to a safe water level based on inventory information, identifying a target computing power resource group corresponding to a low-priority label and performing instance release operation; and responding to a user resource creation request based on the resource after the instance release operation. According to the invention, reasonable resource scheduling can be carried out, the stability of the key task is ensured, and the service quality and the user experience are improved.

The invention provides a multi-factor collaborative dynamic priority task scheduling method based on waiting time feedback, which is characterized by comprising the following steps of: initializing waiting time, task criticality and weight values of queue length, initializing a mixed task set and a task waiting queue, and initializing the task waiting queue into an empty set; entering a task waiting queue; updating the priorities of all tasks in the task waiting queue; sorting the tasks in the task waiting queue according to the priority; monitoring whether a new task arrives in real time; judging whether the newly arrived task meets a preemption condition or not; updating the historical average waiting time of the task; updating weight values of task waiting time and queue length; and judging whether all tasks in the mixed task set are scheduled. According to the method, long-term hunger of low-priority tasks is avoided, and the average waiting time of the tasks is shortened; the preemption threshold value is dynamically adjusted, and the context switching overhead is reduced; a dynamic weight mechanism is designed, and the self-adaptive capacity of the system is improved.

The invention discloses a coroutine-based micro-service gateway dynamic scheduling method, which comprises the following steps that: 1, a coroutine pool is created to process a network request, and a gateway receives the network request and adds the network request into a request queue; 2, identifying a request type, distributing a preset static priority for the network request, and adjusting the dynamic priority in combination with features on the basis of the static priority and the configured initial dynamic priority; and 3, collecting data in coroutine operation in real time, and reducing coroutine processing delay by calculating an overtime threshold, adopting an overtime differentiation strategy, a low-priority discarding strategy and updating a data state according to a data operation condition. The invention further discloses a dynamic scheduling micro-service gateway system based on the coroutine. The method can significantly improve the gateway performance, reduce the system delay, intelligently manage the request queue under a high load condition, ensure the normal operation of key services, and can be widely applied to the field of computer data.

The invention discloses a coroutine scheduling method, device, system and equipment, a storage medium and a product, and relates to the technical field of task scheduling, a multi-priority queue group is used for storing a first coroutine task to be executed in a corresponding scheduling domain, and a storage module is used for storing a second coroutine task periodically executed in a corresponding scheduling domain; the execution cycle of the first coroutine task is smaller than that of the second coroutine task. And when the expired second coroutine task exists in the storage module, mounting the expired second coroutine task to the multi-priority queue group. The situation that the first coroutine task with the low priority cannot be executed due to the fact that the second coroutine task does not reach the execution time is effectively avoided. And the coroutine tasks are obtained and executed from the multi-priority queue group according to the priority sequence, so that the tasks with high priorities are ensured to preferentially obtain execution resources. According to the running state and the load state of each scheduling domain, task migration of each scheduling domain is carried out, and the resource utilization rate is optimized.

The invention discloses a meta universe digital asset online interaction method based on a three-dimensional engine, and relates to the technical field of meta universe. According to the method, an interaction data set is constructed, and the interaction weight of assets is calculated based on the interaction frequency and the interaction duration; according to the method, assets are dynamically divided into high-priority interactive assets, middle-priority interactive assets and low-priority interactive assets according to a set threshold value, interactive data are periodically updated and the priority of the assets is adjusted in real time in an interactive process, so that the high-priority interactive assets always keep high-precision rendering, the low-priority interactive assets are only loaded when necessary, and the rendering efficiency is improved; in the dynamic asset loading and scheduling process, interaction hotspots of users are predicted based on user view angles, moving directions and interaction tracks, a view cone area is established, a future view is calculated in combination with user historical moving data, assets about to enter the view are loaded in advance, interaction hotspot distribution of multiple users is fused in a multi-user online environment, and the dynamic asset loading and scheduling process is achieved. And the high-interaction area is loaded preferentially, so that the resource overhead is reduced.

The invention discloses an AI task scheduling method and system based on a video analysis algorithm. The method comprises the steps that the running state of current server resources is judged; and if the running state of the current server resource is a peak state, dynamically adjusting the detection frequency of a low-priority video analysis algorithm and controlling the low-priority video analysis algorithm to identify the start and stop of the task according to the total computing power of the current task, the total concurrent number of the current task and the running information of different priority algorithms. According to the method, the detection frequency and start-stop of the low-priority video analysis algorithm are dynamically adjusted, so that the efficient scheduling requirement of server resources in a peak state is met.

The invention is suitable for the field of business data scheduling, and provides a data-in-channel scheduling method and system adaptive to multiple business scenarios, and the method comprises the steps: collecting business feature data in real time, abstracting business departments participating in scheduling as game roles, and distributing resource demand attributes and game chip attributes for the game roles. And based on the task urgency degree coefficient, the resource utilization efficiency and the scene adaptation weight, constructing a bid calculation model to generate automatic bids, allocating exclusive resources to high-priority roles in combination with a resource allocation thermodynamic diagram, and scheduling low-priority roles as required through an elastic resource pool. The system dynamically monitors resource utilization rate and task indexes, evaluates bidding rationality and adjusts chip attributes. According to the method, roles are stimulated to compete efficiently through a game mechanism, resource allocation is optimized in combination with dynamic feedback, the problems that a traditional method is poor in adaptability and not public in allocation are solved, and the resource utilization efficiency and service continuity in a multi-service scene are remarkably improved.

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may separately encode lower priority uplink control information (UCI) and higher priority UCI, and multiplex the encoded lower priority UCI and the encoded higher priority UCI on the same physical uplink channel (e.g., a physical uplink control channel (PUCCH)). The UE may be configured with multiple coding rates, which the UE may select from based on a payload size associated with each of the lower priority UCI and the higher priority UCI. Alternatively, the UE may be configured with a separate set of coding rates for the lower priority UCI and a separate set of coding rates for the higher priority UCI. The UE may multiplex the lower priority UCI and the higher priority UCI by mapping to physical uplink channel resources based on an order (e.g., mapping higher priority UCI followed by mapping lower priority UCI).

Systems and methods for enhanced Physical Uplink Control Channel (PUCCH) power control when missing Uplink Control Information (UCI) of different priorities are provided. In some embodiments, a method performed by a User Equipment (UE) for determining a power adjustment includes determining a PUCCH power adjustment with respect to a PUCCH format and / or Transport Format (TF); wherein determining the PUCCH power adjustment is based on one or more out of: a UCI consists of UCI with same priority index or different priority indices; a number of high-priority and / or low-priority UCI bits; and a minimum of high-priority and low-priority UCI bits. In this way, PUCCH power is able to be properly adjusted when UCI consists of UCI with different priority index.

A system may receive a request to generate an object. A system may generate, based on the request, a first portion of a prompt associated with a first layer of a hierarchy and a second portion of the prompt associated with a second layer of the hierarchy, where the second layer has a lower priority than the first layer. A system may generate the object based in part on providing the portions of the prompt as input to a machine learning model, where the object is formatted according to an object format and is internally consistent. To generate the object the machine learning model does not violate instructions associated with a layer of the hierarchy based on instructions associated with a layer of the hierarchy having a relatively lower priority.

The invention discloses a method for storing data in a FLASH in real time, and belongs to the technical field of embedded systems. According to the method, the erasing operation of the FLASH is automatically executed by an embedded system, a mode of circularly storing data according to an address sequence is adopted, and meanwhile, the next sector adjacent to the sector which is being written is erased. A real-time operating system is used for scheduling erasing operation and storage operation, erasing tasks with low priorities and writing tasks with high priorities are created, event marks are matched, and data are stored in real time. The method has the advantages that the conflict between writing and erasing operations is avoided through task scheduling without the control of an upper computer, and the embedded system can automatically store data in the FLASH in real time.

A user equipment (UE) is described that includes a higher layer processor configured to determine that a high-priority channel collides with a low-priority channel. The high-priority channel and the low-priority channel are uplink channels. The higher layer processor is also configured to drop at least a portion of the low-priority uplink channel. The UE also includes transmitting circuitry configured to transmit the high-priority channel.