18 results about "Autoscaling" patented technology

This invention discloses a cloud-native method for dynamic resource scheduling and elastic scaling management of mini-programs, specifically relating to the fields of cloud-native and microservice resource scheduling. The method involves: First, collecting multi-dimensional operational metrics in a non-intrusive bypass manner via a sidecar proxy; second, inputting the filtered and filled metrics into a hybrid predictor to predict resource demand and calculate a burst factor; third, designing a two-layer collaborative scheduler that combines horizontal elastic scaling with vertical dynamic adjustment based on reinforcement learning; and fourth, introducing a multi-tenant fair arbitration mechanism based on resource claims, prioritizing the expansion requests of less-used tenants by sorting them in ascending order of claim value; finally, encapsulating the decision into a custom resource object for execution, and performing incremental model training and closed-loop optimization based on the feedback error sequence.

The application relates to the technical field of distributed simulation based on containerization deployment and elasticity, and discloses a distributed simulation method and system based on containerization deployment and elasticity, which comprises the following steps: collecting real-time resource monitoring and historical load trend data of a simulation task, and combining simulation calculation complexity parameters and I / O density parameters to perform unified load evaluation, so that fine modeling of the simulation task and resource demand prediction are realized, and the accuracy of task scheduling and distribution is improved; through subtask segmentation based on the minimum executable unit and a standard containerization packaging mechanism, and in cooperation with a container arrangement platform, modular deployment of the simulation task is realized, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The application provides a Presto scheduling-based elastic scaling method and device. The elastic scaling method comprises the following steps: in the case that a query request needs to be independently run and is a core service, obtaining a target performance index of an initial cluster and target query information of the query request, wherein the target performance index at least comprises memory usage and CPU occupancy of the initial cluster, and the target query information at least comprises metadata information and context information; and performing elastic scaling on the initial cluster according to at least the target performance index and the target query information. The application realizes dynamic arrangement and control of resources, alleviates the queuing problem caused by resource competition, avoids the problem that the number of PODs is uncontrollable caused by the elastic scaling taking only the CPU and memory usage (i.e. the target performance index corresponding to the initial cluster) as the monitoring index, and achieves the purpose of improving the stability and resource utilization of the service.

The application relates to the technical field of application server middleware performance test, and particularly discloses an application server middleware performance test system. The system comprises a business scene intelligent modeling module, an adaptive load generation and scheduling module, a full-stack performance data acquisition and correlation analysis module and a test environment automatic management module. Through the cooperative work of the above modules, the test script is automatically generated from natural language or logs, the load pressure is dynamically adjusted based on real-time feedback, the full-stack performance data correlation analysis and root cause positioning are realized, and the test environment is automatically constructed and elastically expanded, so that the efficiency, authenticity and automation level of the performance test are improved.

The application discloses a multi-agent collaborative resource elastic scaling decision system for intelligent operation and maintenance, a multi-agent collaborative sensing module, which completes multi-dimensional data collection and standardized preprocessing, and provides standardized data input for subsequent processes; an intelligent decision and prediction module, which carries out load prediction and scaling strategy generation based on the preprocessed data; an execution scheduling and feedback optimization module, which executes the scaling strategy, and realizes continuous iteration of the prediction model and the decision model based on the execution effect, forming a closed loop of 'execution-evaluation-optimization'. The multi-agent collaborative resource elastic scaling decision system for intelligent operation and maintenance can reduce operation and maintenance costs while ensuring stable operation of services, and effectively improves the intelligent level of the operation and maintenance system.

This invention relates to the fields of edge computing and distributed machine learning technology, specifically disclosing a heterogeneous edge large-scale model collaborative training method based on nested manifold alignment. By introducing a nested heterogeneous compatible architecture guided by singular value decomposition, an ordered spectral basis is constructed in the parameter space, enabling in-vehicle intelligent terminals with different computing power to train different spectral slices of the large model separately. This achieves elastic scaling for inference across the entire spectrum of in-vehicle intelligent terminals with a single global collaborative training. Secondly, to address gradient conflicts in multiple tasks, an energy and density-aware parameter-semantic dual-stream spectral aggregation mechanism is designed. This mechanism uses orthogonal spectral projection to decouple complex mixed gradients into independent feature components, eliminating destructive interference at the microscopic level. Simultaneously, it uses singular value energy density as a physical prior to robustly calibrate long-tail noise, breaking down the dual barriers of heterogeneity and task conflicts in in-vehicle intelligent terminals.

The application discloses a kind of container application automatic deployment and elastic scaling method.Responding to deployment request, obtain and parse engineering build file and / or engineering configuration file, extract engineering features, automatically generate image construction description information and deployment arrangement configuration for target environment, build application image and create running instance in container arrangement platform.Resource indicators and application side runtime indicators are collected during running, and associated time series are formed according to instance identification and time stamp, cross-domain indicator mutual correlation peak value, correlation strength and time lag change are calculated in sliding window, abnormal score / label is output according to correlation decay and lag mutation and is classified as resource saturation or load growth, respectively, resource specification dynamic calibration or replica elastic scaling is performed, and minimum interval / hysteresis is set to suppress oscillation.Adjustment uses rolling update, readiness detection failure rollback and stability monitoring rollback after adjustment.

This invention belongs to the field of software refactoring and serverless computing technology, specifically relating to a fine-grained service decomposition and dynamic deployment method for object-oriented applications in edge environments. By constructing a method call graph through static analysis, and for the first time applying a hybrid heuristic algorithm combining particle swarm optimization and genetic operators to this graph partitioning problem, it can intelligently find a decomposition scheme that minimizes cross-function communication overhead while satisfying the limited resource constraints of edge nodes. This fundamentally solves the contradiction between computing resources and communication latency that traditional decomposition methods struggle to balance. Furthermore, by introducing Function-as-a-Service (FaaS) proxies and wrappers, it achieves automated refactoring of the original object-oriented code, enabling seamless migration of tightly coupled local calls to a distributed FaaS environment. This significantly reduces the technical threshold and operational complexity of application migration, achieving fine-grained resource utilization and elastic scaling.

Automated scaling-related operations may be performed dynamically during execution of a spatial simulation. A spatial partition may be locally reassigned, based on application workload information, from a first application to a second application on the same worker. A quantity of applications on a worker may also be changed during execution of a spatial simulation. A parent spatial partition may be split into child spatial partitions, and child partitions may also be merged back into a common parent partition. Indications of partition splits and merges on each of a plurality of workers may be reported to the plurality of workers. A spatial partition may also be remotely reassigned from a first worker to a second worker, such as based on worker-level resource consumption information and partition information. A quantity of workers that are used to implement a spatial simulation may also be changed during execution of the spatial simulation.

The application discloses an ETL dynamic horizontal extension method for time series data, which comprises the following steps: receiving an ETL task definition described in a declarative language through a job management node MNode; persisting definition information and a state of the task into a high-availability time series database TSDB based on a consensus algorithm; distributing the task to one or more stateless execution nodes XNode according to a scheduling strategy by the job management node MNode; executing the ETL task by the stateless execution node XNode; and realizing elastic scaling of system processing capacity by dynamically registering or unregistering the stateless execution node XNode in response to system load changes.

The application relates to the technical field of big data processing, and discloses a resource dynamic scheduling method of a big data platform system architecture and a supporting environment, which comprises the following steps: acquiring task description information and performing semantic analysis; identifying the resource type range and data access mode required by workflow execution; identifying key tasks; evaluating the matching degree between each task and each resource node, and performing weighted calculation in combination with key path analysis results; solving an optimal mapping scheme of the task to the resource node; locking the computing resources of the key path task; predicting data flow and identifying resource bottlenecks; dynamically adjusting resource allocation; performing task scheduling and resource scaling operation; monitoring the execution state in real time, and performing deviation analysis and dynamic adjustment of parameters; and the application is based on a timeliness constraint guarantee mechanism of workflow key path analysis and an active prediction type elastic scaling strategy of data sensing, can accurately identify key tasks affecting the overall completion time and provide resource priority guarantee for the key tasks.

The application discloses a kind of software operation management systems based on intelligent cooperation, it is related to software operation management technical field, including data acquisition layer, data fusion layer, intelligent cooperation engine layer, core business operation layer, adaptive process engine layer, multi-role cooperation layer, decision support layer and system support layer.The software operation management system based on intelligent cooperation, the automation execution of the cooperation of each module and operation process is realized by intelligent cooperation engine layer, cross-module operation task can be completed without manual intervention, reduces manual operation link, reduces labor cost;And the efficient cooperation mechanism of multi-role cooperation layer, the processing period of work task is shortened, and the operation efficiency is further improved;Through the fusion prediction model and multi-objective optimization algorithm of intelligent resource allocation module, accurately predict resource demand, generate optimal resource allocation scheme;Combined with resource dynamic adjustment unit realizes the elasticity of resource, avoids the situation of resource excess or deficiency.

This invention presents an integrated architecture and method for command and control decision-making services based on the MCP protocol. It standardizes and encapsulates command and decision-making functional modules into independent MCP services according to the MCP protocol, achieving unified input / output formats and communication standards. Service registration, version management, load balancing, and unified scheduling are achieved through an API gateway. A visual workflow orchestration platform is provided, supporting drag-and-drop node configuration and automatic parameter mapping. Cross-language communication is achieved using the Stdio transport layer, eliminating the need to expose network ports and enhancing security. Each MCP service can be deployed independently, hot-updated, and elastically scaled, significantly reducing system coupling. This invention achieves "plug-and-play" integration of command and decision-making services, enabling rapid access and unified invocation of multi-source functional modules, greatly improving the flexibility and maintainability of the command and decision-making system. It is applicable to command and control scenarios such as command and decision-making, emergency dispatch, and situational awareness, providing core technical support for building a scalable and collaborative intelligent command and decision-making application ecosystem.

The application discloses a GPU resource optimization method and system based on dynamic prediction and topology heat map. The method comprises the following steps: constructing a server heat map, the heat value of which is calculated based on historical task characteristics and node topology connection edge quantity weighting; predicting future integrated GPU resource demand based on a to-be-scheduled task queue and historical task data to generate a resource demand heat map; quantitatively identifying GPU resource fragmentation risks based on the resource demand heat map and a dynamic reference value calculated based on the server heat map; selecting an optimal optimization strategy in response to the fragmentation risks; and executing the selected strategy, including dynamically migrating tasks supporting checkpoints and / or scaling resources of tasks supporting elastic scaling, to realize lossless reconstruction of GPU resources. Through the intelligent closed loop of prediction-decision-execution, the application actively resolves resource fragmentation, and significantly improves the resource utilization and task scheduling efficiency of a large-scale GPU cluster.

The application discloses a micro-service tail delay prediction method based on unified representation learning, comprising the following steps: collecting traffic side features, resource side features and real micro-service tail delay; constructing a micro-service tail delay prediction model: taking the traffic side features and the resource side features as inputs; using GNN to extract the traffic side features to obtain traffic side representation vectors; using gMLP to extract the resource side features to obtain resource side representation vectors; fusing the traffic side representation and the resource side representation to output a micro-service tail delay prediction result; taking the real micro-service tail delay as a label, training the micro-service tail delay prediction model to obtain a trained micro-service tail delay prediction model; collecting the traffic side features and the resource side features of a micro-service system to be predicted and inputting the features into the trained micro-service tail delay prediction model to output a tail delay prediction result of a next time window. The method significantly improves the accuracy and robustness of window-level tail delay prediction and enhances the elastic scaling decision effect of SLO guarantee.

The application discloses a logistics management system development method based on service demand big data analysis, and relates to the technical field of logistics management.The application significantly improves the intelligent level and development efficiency of the logistics management system by constructing a full-link automatic development system.Firstly, ST-DBSCAN and KDE kernel density estimation algorithms are used for deep data cleaning and feature extraction, and a high-dimensional feature matrix generated by PCA dimension reduction is combined to accurately depict the space-time evolution law of the logistics network.Secondly, an LSTM and XGBoost stacked ensemble model is used to capture the time sequence dependence and static attribute coupling relationship, greatly reducing the MAPE and RMSE errors of resource demand prediction, and realizing accurate transport capacity prediction.The system can automatically generate a micro-service code framework and an HPA elastic scaling strategy based on the predicted peak value, realize adaptive adjustment of the architecture, and avoid resource waste and service overload.In summary, the application shortens the system development cycle and improves the system throughput and stability in a high-concurrency scenario.