134 results about "Resource contention" patented technology

The invention discloses a cloud side-end cooperative task scheduling and efficiency optimization method and system for heterogeneous patrol resources, and relates to the technical field of intelligent scheduling and resource optimization. According to the method, accurate perception of a resource state is realized by constructing a digital twinborn and federated learning mechanism, resource contention conflicts are solved by adopting a space-time diagram attention network and multi-agent reinforcement learning, and multi-target optimization and trusted execution are realized in combination with a quantum genetic algorithm and a block chain smart contract. Finally, the stability of the system is verified through Lyapunov optimization, a complete scheduling system from resource perception and conflict resolution to steady state maintenance is formed, and the task scheduling efficiency and the system stability in the heterogeneous resource environment are remarkably improved.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention provides a containerized resource elastic scheduling method and system in a cloud computing environment, and relates to the technical field of containerized resource elastic scheduling, and the method comprises the steps: obtaining various resource contention events, and generating data which records a resource contention relationship between tenants in detail; identifying a preset high-frequency conflict mode by deeply analyzing the data, and determining a specific tenant group with a strong resource mutual exclusion effect; dynamically estimating a probability level of resource conflict between the new container and the specific tenant group in combination with historical contention information and a real-time resource load state; generating a set of dynamic isolation scheduling strategies according to the conflict risk indicated by the probability level; an independent conflict buffer resource pool is configured and adjusted for a specified key tenant, a tenant resource conflict mode can be identified, dynamic isolation scheduling and buffer resource configuration can be implemented, and the resource utilization efficiency and the system stability of a multi-tenant container in a cloud computing environment are improved.

The invention relates to the technical field of electric digital data processing, and discloses a data processing system for message management, which comprises a look-ahead buffer unit for maintaining a sliding window with variable window size parameters and pre-reading a message sequence; the mapping logic circuit is used for converting the resource identifier into a resource request mask; the state register is used for storing a global occupancy bitmap for representing resource occupancy; the scheduling controller is used for distributing messages according to bit operation results, counting bitmap setting quantity based on the population counting instruction to generate a resource saturation index, and then adjusting window size parameters in a negative feedback mode. The resource contention entropy value is directly sensed by utilizing a hardware instruction, nanosecond-level self-adaptive breathing control of a scheduling window is realized, and the service life of the scheduling window is prolonged. The problem of physical computing power collapse caused by bitmap scanning jitter in a high-concurrency scene is solved.

The invention provides a complex scene-oriented multi-modal AI Agent integrated development method and system, and the method comprises the steps: generating a layout graph G in an integrated development environment, and carrying out the consistency verification to obtain a constraint set C; a graph neural network is adopted to represent the arrangement graph to obtain graph embedding Z, and an environment state s is formed by combining key performance indexes in a sliding window; and in an action space AC limited by the C, jointly deciding computing power node selection, concurrent resource quota adjustment and semantic caching or semantic routing parameter configuration according to a reinforcement learning strategy pi, and executing actions to optimize throughput, tail delay, failure rate and resource contention degree. Through a closed loop of verification-constraint-learning-execution-monitoring-updating, cross-modal consistency and computing power scheduling are integrally optimized, training convergence and online stability are improved, long-tail time delay and the failure rate are remarkably reduced, the resource utilization rate is improved, and the method is suitable for large-scale popularization and application. The method is suitable for the fields of medical diagnosis, industrial quality inspection and equipment maintenance, retail recommendation and shopping guide and the like.

The invention discloses a large model mixed load-oriented self-adaptive low-delay reasoning configuration generation method and device, computer equipment and a storage medium, and the method comprises the steps: determining a first token generation delay and an adjacent token delay interval which are historically configured on requests with different reasoning configurations, and obtaining tuples to form a configuration performance database, generating a delay prediction model by combining a least square method with the configuration performance database; dynamically dividing the historical request into a plurality of buckets according to the input length and the output length through a self-adaptive bucket dividing strategy; a configuration generator generates reasoning configuration for each bucket according to the input length and the output length of the historical request of each bucket; under the real mixed load, the problems of remarkable resource contention, queue head blockage, KV Cache switching overhead increase and the like are avoided in concurrent execution of long and short requests, and meanwhile, the problem of tail delay amplification is avoided, so that a reasoning system gives consideration to low delay and high throughput among different requests.

The invention provides a real-time monitoring and root cause positioning method and system for software defects. The method comprises the following steps: collecting real-time monitoring data of a software processing thread, and generating a thread competition thermodynamic diagram for identifying a shared resource contention point through time sequence association processing; triggering a server memory bus embedded hardware detection device based on a contention point to dynamically capture a locking state transition signal, and converting the locking state transition signal into a locking event sequence; constructing a locking contention track diagram according to timestamp sorting, and completely displaying a process path of a locking mechanism from obtaining to releasing; constructing a dependency topological structure of the thread and the lock based on the trajectory graph, and identifying a resource contention chain; and mapping the contention chain into an unsynchronized shared variable access path in the source code, and triggering a real-time alarm signal. According to the method, the access path of the unsynchronized shared variable is accurately positioned, the lock competition diagnosis efficiency is improved, and the system deadlock risk is zeroed.

The invention relates to the field of unmanned aerial vehicle control, in particular to a multi-mode link switching method for a heterogeneous unmanned aerial vehicle cluster and related products, and the method comprises the steps: obtaining the current task mode information of the heterogeneous unmanned aerial vehicle cluster and the link state information of each node; dynamically selecting and activating a corresponding link switching strategy from a preset strategy set according to the task mode information; according to different task modes, different link switching strategy sets are dynamically selected, under the constraint condition that communication resources are strictly limited, the resource contention contradiction in the heterogeneous unmanned aerial vehicle cluster is solved, and the communication toughness and efficiency of a cluster system are improved.

The invention discloses a computing node power consumption monitoring and predicting method, and belongs to the technical field of computers. The problem that an existing power consumption prediction method is poor in accuracy is solved. According to the method, the dynamic operation states of the nodes, the hardware static configuration attributes and the coupling influence of the communication relation and power consumption between the nodes are comprehensively considered, a graph structure based on node communication is constructed, and a power consumption coupling path between the nodes is modeled and calculated, so that the dynamic energy consumption of local nodes can be captured; and cross-node resource contention, communication bottleneck and energy consumption conduction effect can be considered. Through a structure-guided graph attention mechanism, the model can realize information sharing and feature collaboration among a plurality of nodes. By introducing an energy efficiency attention fusion module, dynamic weighted modeling is performed on multi-channel input features, key factors having high influence on energy consumption fluctuation are automatically identified, and the accuracy and interpretability of the model in the aspects of power consumption prediction and optimization suggestion output are improved. The method can be applied to power consumption prediction of the computing node.

A GEMM load-oriented GPU modeling method is characterized in that through a multi-stage collaborative modeling mechanism, cache behaviors, instruction overhead and calculation intensity are deeply coupled, accurate performance prediction of GPU execution GEMM operators is realized, the method can be widely applied to scheduling optimization of GPU intensive scenes such as AI training and scientific calculation, firstly, a three-stage cache weight distribution mechanism is established, and then, a three-stage cache weight distribution mechanism is established; quantifying the contribution of the L1 / L2 cache hit rate and the DRAM bandwidth degradation factor to the effective bandwidth; secondly, an instruction-level memory access overhead correction mechanism is introduced, and the mixing precision and the real calculation strength of a sparse calculation scene are captured through dynamic parameter adjustment and optimization; then combining the calculation force peak value and the bandwidth upper limit to construct a double-boundary constraint model, and generating a theoretical performance critical value; further predicting a stream multiprocessor utilization rate based on a neural network, and quantifying efficiency loss caused by hardware resource contention through a multi-layer perceptron structure; and finally, the integration module outputs task execution time to realize end-to-end performance prediction.

The invention discloses a power distribution automation terminal task scheduling method and device based on a power distribution network edge computing node, electronic equipment and a storage medium, and belongs to the field of power distribution network automation, and the method comprises the steps: obtaining the total amount of special and public containers of an edge node and the decomposition configuration and operation parameters of a target power distribution automation task; the method comprises the following steps of: decomposing a task into micro-applications, constructing a calling graph, counting the repetition degree of calling edges, combining the edges with high repetition degree into an undetachable micro-application chain, and calculating and distributing exclusive containers according to the repetition degree and the total number of the exclusive containers; and calculating weights according to task calling times, priorities and concurrency numbers, and calculating and distributing the public containers based on the weights and the total number of the public containers. Therefore, by implementing the method and the device, the problems that high-frequency fixed service flow resources are severely scrambled and the utilization rate of discrete task resources is low in the prior art can be solved.

The invention discloses a multi-disciplinary simulation task intelligent planning method based on a knowledge graph, and relates to the technical field of intelligent scheduling, and the method comprises the steps: constructing a preliminary knowledge graph comprising multi-disciplinary task description, resource limitation and execution dependence, analyzing historical task data through a pre-trained deep learning model, and obtaining a multi-disciplinary simulation task planning model; dynamically adjusting the preliminary knowledge graph through adaptive learning, and outputting an adaptive knowledge graph; carrying out resource redistribution optimization according to the updated task scheduling strategy, continuing to execute the task, starting monitoring, and collecting task progress and resource data as a new feedback data stream; the resource reallocation and the new feedback data stream are fed back to the adaptive knowledge graph, and multidisciplinary task description, resource limitation and execution dependency are updated through reasoning to generate a final optimization task execution sequence; the synchronous suppression of waiting time and resource contention is realized, the average utilization rate of resources and the time sequence stability are improved, and the adaptive adjustment capability of operation disturbance is formed.

The application discloses a kind of based on flow perception intelligent network interface card optimization system and method, it belongs to computer network technical field.The optimization system of the application includes flow perception module, flow prediction module, resource scheduling module, priority queue module and load balancing module.The application introduces resource monitoring, flow perception, resource prediction and dynamic scheduling module, real-time monitoring network flow and predicting its change trend, according to flow mode dynamically adjusts resource allocation.The application effectively solves the problem of resource contention, through priority ordering and load balancing, ensure the stable operation of high priority task, improve the throughput and stability of system, reduce resource waste.The application is suitable for resource management of intelligent network interface card in data center and cloud computing environment, improves network processing efficiency and resource utilization, guarantees service quality, reduces resource waste, and enhances the adaptive capacity of system to dynamic load and complex flow mode.

The invention provides a method for realizing an intelligent non-player character in an interactive game, which relates to the field of data processing and comprises the following steps of: packaging a decision intention into an immutable action package in a predefined decision period and placing the action package in a time domain trusteeship queue to realize time decoupling of a decision and a runtime load; the world state projection is divided into an opaque decision phase and a reconciliation phase, the decision phase only exposes the event category projection and the historical abstract, and the reconciliation phase reflows detailed trigger details in an independent window for posteriori maintenance but does not backtrack submitted actions; submission follows an atomization criterion and the submitted abstract does not contain time information; during resource contention, accumulating in response to a debt, and repaying through display shaping on the premise of not exposing a time sequence so as to keep the semantic consistency of main games; the posterior processing updates the action banks and event mappings offline based on the backflow details to improve subsequent decisions.

This disclosure provides embodiments of a method, apparatus, and electronic device for dispatching supplies for grassroots governance matters. One specific implementation of the method includes: in response to detecting a supply dispatch request for a grassroots governance matter characterizing a public emergency, acquiring corresponding matter information; performing information standardization processing on the matter information to obtain standard information; extracting target elements from the standard information to obtain a target element information set; performing resource contention conflict detection on the target element information set to obtain a resource contention index set; in response to the resource contention index set reaching a preset threshold, dynamically prioritizing the target element information set based on the resource contention index set to obtain a priority index set; generating a resource allocation instruction based on a hardware rule engine with pre-set resource arbitration rules and the priority index set; and performing supply dispatch operations according to the resource allocation instruction. This implementation achieves the technical effect of reducing the response delay of supply dispatch operations.

The invention relates to a control method and device for a disk array, and belongs to the technical field of new generation information technology industries. In thread pool parallelization, a serial verification step is divided into a plurality of independent tasks, the independent tasks are executed in parallel by different threads, and parallel verification is performed through a thread pool, so that the total delay is reduced, and the requirements of a high-priority module are met; thread blockage and resource competition are avoided, and thread safety of task distribution and result processing is ensured; the DMA transmission mode and the verification type are adjusted according to the request priority, hardware changes such as disk faults and bandwidth fluctuation can be responded through dynamic selection of the DMA transmission mode and the verification type, the real-time performance and the resource utilization rate are balanced, the switching overhead is reduced through thread pre-binding, and the safety is improved through multi-dimensional verification.

This invention relates to a method and electronic device for obtaining blocking information of an uninterruptible sleep state of a process, belonging to the field of computer technology. The method includes: when a process triggers a lock contention start tracking point due to resource contention, determining the lock type by parsing the lock contention start tracking point parameters; in response to the lock type being a mutex lock, obtaining the lock address by parsing the lock contention start tracking point parameters; clearing the flag bits in the owner member of the kernel lock structure pointed to by the lock address by performing bitwise operations, obtaining a pointer to the process descriptor of the holder; obtaining the process identifier and process name of the mutex lock holder by accessing the pointer to the process descriptor of the holder; and storing the process identifier, process name, and lock address of the mutex lock holder in a task blocking context structure with the process identifier of the mutex lock holder as the key, thereby achieving accurate acquisition of the mutex lock holder information.

The invention relates to the technical field of docking station remote control, in particular to a cloud management-based micro host docking station remote control system, which comprises a multi-tenant resource slice configuration module for receiving a multi-tenant configuration instruction according to a cloud management platform and establishing a virtual docking station resource allocation table. According to the method and the device, the specific USB port, the specific HDMI port, the specific power budget and the specific network bandwidth limit value are bound with the unique tenant voucher by establishing the virtual docking station resource allocation table, so that a shared physical device is converted into a plurality of logically independent virtual devices, the problems of resource scrambling and security interference in a multi-tenant environment are solved, and the service life of the virtual docking station is prolonged. And when the behavior of one tenant, such as connection with high-power consumption equipment or malicious USB equipment, is strictly limited in the allocated resource quota, and does not affect other tenants, so that the stability and the safety of the platform are ensured.

The invention discloses a method and system for deploying a multi-client operating system for preventing resource contention, and the method comprises the steps: generating a grouping information table and a resource distribution table based on the business demands and the total number of hardware resources of a multi-core processor system, the number of the processing groups is configured to be smaller than or equal to the total number of hardware resources which can be independently divided in the multi-core processor system, and the resource allocation table is used for storing a plurality of pieces of hardware resource allocation information corresponding to the processing groups; dividing the plurality of processing cores into each processing group based on the service requirements of the multi-core processor system and the grouping information table; and based on the resource allocation table, performing hardware resource configuration on each processing group so as to obtain a plurality of hardware resource isolated client operating systems. Based on the deployment method, isolation of hardware resources among multi-client operating systems can be realized, and the performance and resource occupation of a multi-processor system are optimized.

This invention provides a method, storage medium, and device for collecting database statistical information. The method for collecting database statistical information includes: initiating statistical information collection; obtaining log information since the last statistical information update; filtering modified data information related to the statistical information from the log information; and updating the statistical information based on the modified data information. By collecting statistical information through accessing log information, there is no need to access the tables, thus avoiding resource contention with user operations, reducing the burden of table access, and consequently minimizing the impact on database processing performance.

The invention relates to a task scheduling method, system and device based on a multi-core processor and a medium. In the application, cores of a processor are divided into different core pools, and when a target task sent by a user side is executed, a target core can be selected from the corresponding core pool to execute the target task based on priority information of the target task; through the mode, core resources in different core pools can be provided for tasks with different priorities, so that core resource competition is reduced, core scheduling distribution is planned, mutual interference among the tasks in the core pools is reduced, and the real-time task response capability is improved; when the target task is executed, if it is detected that the user side is in the inactive state, the target core is automatically released, core resources are prevented from being occupied, and the resource utilization rate is increased; moreover, when the user side is reconnected, the target task can be continuously executed according to the task operation record, the working state of the uncompleted task is recovered in time, and resource waste caused by repeated execution of the task is avoided.

This invention relates to the field of fault diagnosis technology, and in particular to an intelligent operation and maintenance method for the entire IT system chain for precise fault location. It constructs a dual dependency topology including explicit logical call edges and implicit resource contention edges, simultaneously covering business call associations and resource contention associations within the same host machine. This eliminates the blind spots in traditional solutions and is adaptable to complex fault scenarios involving business call anomalies, resource contention anomalies, and both. A micro-level queuing entropy increase rate quantification model is constructed. Through integral calculations of system scheduling saturation and effective instruction execution rate, it achieves refined quantification of micro-level anomalies at the kernel scheduling level, enabling early detection of fault precursors and accurate differentiation between native node anomalies and propagated anomalies. Starting from the alarm node, it traverses all associated nodes in reverse, and adjusts the weights of nodes with different anomaly types based on the node's micro-level queuing entropy increase rate, accurately quantifying the true fault contribution of each node.

This invention belongs to the field of distributed caching technology and relates to a dynamic hotspot data migration method for distributed caching systems. By acquiring multi-dimensional operational status data in real time and generating a real-time status dataset, this invention overcomes the lag and inadequacy of traditional static sharding strategies in dealing with hotspot data, improving the accuracy of system load distribution perception. It establishes a dynamic linkage mechanism between hotspot prediction, migration cost simulation, and node resource allocation. Based on access trend parameters and changes in node instantaneous processing capacity, it differentiates and adjusts data migration paths and synchronization strategies, achieving optimal matching between hotspot data distribution and node resource utilization, as well as a dynamic balance between service performance and stability. Through background incremental synchronization and traffic collaborative control, it monitors the distribution of read and write requests during the data migration process in real time and continuously optimizes the migration plan based on objective indicators, avoiding service jitter and resource contention during migration.

The present application relates to the technical field of cloud computing and virtualization, and particularly to a cloud service resource scheduling system based on virtual machines; comprising benchmark construction, parallel differential analysis, feature matching and scheduling execution modules; the system collects real-time indexes to generate ideal values without interference, and calculates real deviation and theoretical deviation based on interference models; the core is to calculate the similarity of the two types of deviation in the feature space, and accordingly to distinguish resource contention and business increment, to perform cross-machine live migration when the similarity meets the standard, and otherwise to perform in-place expansion; the present application converts abnormal diagnosis into a pattern matching problem, accurately identifies the root cause of performance degradation, and effectively avoids misjudgment and invalid operation of traditional schemes.

This application provides an intelligent routing function optimization method and system based on an intelligent vehicle power domain control platform, belonging to the field of intelligent vehicle electronic control technology. It addresses the problems of resource contention, excessive processor load, insufficient real-time performance, and poor scalability in in-vehicle network routing functions in related technologies. This method employs an architecture that separates the management plane and data plane. It pre-generates routing numbers encoding source, path, and destination information for communication relationships, and quickly extracts information based on these numbers through bit operations to execute routing decisions. Furthermore, it combines doubly linked list cache management, multi-core affinity scheduling, and multi-level resource provisioning strategies to achieve efficient cache resource management, significant optimization of processor load, and an overall improvement in system real-time performance and reliability.

This specification provides a request distribution processing method and a request distribution processing system. The request distribution processing method includes: obtaining file processing requests for target files; determining a target request processing queue set from multiple candidate request processing queue sets based on the request type of the file processing request, wherein the multiple candidate request processing queue sets correspond to different request types; determining a target request processing queue from the target request processing queue set based on the file identifier of the target file, and adding the file processing request to the target request processing queue; and invoking the request distribution resource corresponding to the target request processing queue to distribute and process the target requests in the target request processing queue. This eliminates the risk of read requests being starved due to resource contention, ensuring low-latency response for data loading; and ensures that processing requests for massive numbers of small files can obtain a fair scheduling opportunity, thereby preventing small files from being starved by large files.

The invention discloses a multi-level GPU resource management and scheduling system for a deep learning task, and the system comprises an MIG hardware isolation layer which is used for segmenting and isolating GPU physical resources to form independent sub-examples; the MPS elastic sharing layer is used for dynamically adjusting SM quotas according to task loads and realizing elastic allocation of computing resources; the Kernel-level fine scheduling layer is used for monitoring a Kernel-level resource contention state in real time and ensuring that an online task is executed preferentially; the performance predictor is used for outputting task performance data under different MIG and MPS configurations; and the Hybrid Partition Scheduler is used for selecting an optimal MIG partition combination and MPS quota strategy based on the performance data, and realizing the unification of resource isolation, elastic sharing and real-time scheduling of online tasks through cross-layer collaboration of the above components. According to the method, the problem that resource isolation, elastic sharing and real-time performance are difficult to consider in the prior art is solved, resource fragmentation and inter-task interference are reduced, and the method is suitable for large-scale deep learning training and reasoning services.

The invention relates to the technical field of data processing, in particular to a container-based pressure dynamic adjusting method, which comprises the following steps of: acquiring network protocol calling relation data and service semantic information; constructing a dynamic load portrait comprising protocol calling topology and resource competition characteristics by using a graph neural network; generating a load adjustment strategy based on the dynamic load portrait, injecting an analog flow execution strategy into the mirror image copy of the service system, and recording response delay distribution; when the delay exceeds a threshold value, topology scheduling resources are called to the associated container group according to a protocol, and the pressure measurement task is bound to the container group in the same area; establishing a secure channel and a hardware-level isolation environment according to the position of the container group, and collecting full-link calling data; and positioning performance bottleneck nodes through a causal inference model, optimizing load adjustment strategy parameters, and constructing a pressure measurement scene knowledge base in combination with historical load data to realize strategy migration. The problems of incomplete scene modeling, low high-concurrency simulation efficiency and bottleneck positioning delay of a distributed system are solved.

The invention relates to the technical field of software deployment, in particular to a software deployment method and system based on a distributed architecture. Comprising the following steps: establishing a plurality of service nodes based on system parameters, and constructing a deployment optimization model according to feature parameters of all the service nodes; obtaining state parameters of each service node, generating a deployment demand based on all the state parameters, and generating a primary deployment strategy according to the deployment demand and the deployment optimization model; establishing a monitoring time axis, and judging whether a correction parameter of a first-level deployment strategy is generated or not according to a preset monitoring time node; according to the invention, aggregation processing is carried out on all service nodes, a plurality of node sets are constructed, and a deployment optimization model is constructed according to the characteristics of each node set, so that the complexity of a deployment task is reduced, and the software deployment efficiency of a system is improved. By performing conflict analysis on the initial sub-strategies of each node set, resource contention and strategy conflicts among different node sets are avoided, global coordination is realized, and stable operation of the system is ensured.

The invention relates to the technical field of solid state disks, in particular to a ZNS SSD management command optimization processing method. The method comprises the following steps of: deploying a plug-in at a host end to realize regional information management and IO (Input / Output) truncation processing, dividing a physical region of a ZNS SSD into four queues, namely freezone (idle), invaridzone (ineffective), active zone (active) and finishingzone (to be completed), and dynamically managing the four queues, namely the free queue, the invaridzone (ineffective), the active zone (active) and the finishingzone (to be completed); according to the FINISH instruction, writing pointer information is modified to replace filling writing operation, and meanwhile, a corresponding physical area is added into a finishingzones queue; reconstructing on the basis of a physical region in the finishingzones queue, and mapping a free space of a plurality of regions which are not fully written into a new logic region; a delay execution strategy is adopted for the RESET instruction, and actual erasure operation is triggered only when physical area data is completely invalid or an idle threshold value exists. According to the method, the problems of high time delay, resource contention and unbalanced wear caused by FINISH instruction filling and writing in the ZNS SSD are solved, the interference of a management command on read-write IO is effectively reduced, the utilization rate of a storage space is improved, and the service life of the ZNS SSD is prolonged.