102 results about "Autoscaling" patented technology

The present invention relates to a system and computer implemented method for cost-aware autoscaling of artificial intelligence workloads using predictive queueing models, designed to achieve proactive and economically optimized scaling of computational resources across cloud and edge environments. The invention introduces a predictive queueing-based technique that anticipates future workload congestion by modeling dynamic task arrivals and service times using a stochastic queueing process. A cost estimation unit computes the total projected operational cost of potential scaling actions by integrating real-time infrastructure pricing data, predicted delay penalties derived from service-level objectives, and estimated energy consumption. A scaling decision unit applies reinforcement learning-based optimization to select the scaling action that minimizes total cost while ensuring compliance with latency and throughput constraints. The system includes a hardware-integrated autoscaling controller device comprising a predictive computation processor, cost-decision processor, and scaling actuation interface configured for real-time execution of predictive and scaling operations.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The invention relates to a resource elastic scaling decision-making method, system and device and a medium. The method comprises the following steps: collecting real-time operation data of a security service node, and performing multi-dimensional security index analysis according to the real-time operation data to obtain a portrait data packet; predicting the security service weight value to obtain a prediction result, performing dynamic error compensation on the prediction result to generate a corrected weight prediction value, and generating a control instruction based on the corrected weight prediction value and the active session state; and when the instruction is a migration instruction, analyzing a session state snapshot of the instruction, calling a preset kernel state locking function to lock a memory session block of a source node, obtaining incremental state change data to generate a migration snapshot packet, and performing block verification injection operation on a target node. According to the method, by integrating multi-dimensional safety index analysis, prediction error compensation and stateful transition verification mechanisms, the accuracy and response efficiency of resource elastic scaling decision making are improved, and the continuity of stateful service transition and the consistency of safety strategies are enhanced.

The invention relates to the technical field of browser-side high-performance computing, in particular to a WebAssembly-based high-performance front-end audio and video processing method and system, and the method comprises the following steps: designing a modularized Wasm runtime container, constructing a zero-overhead memory interaction mechanism, and establishing a hardware adaptive scheduling engine; the method has the beneficial effects that dynamic loading of an audio and video algorithm is realized by designing a modularized Wasm runtime container, serialization overhead of communication between threads is eliminated by utilizing a shared memory mechanism, a hardware self-adaptive dynamic scheduling system is created, and finally three core objectives are achieved: execution efficiency close to native codes is provided in a pure browser environment; establishing a standardized processing pipeline without dependence of a third party; intelligent scheduling and elastic expansion and contraction of terminal heterogeneous computing resources are realized, and Web end infrastructure support is provided for professional audio and video applications.

A computer-implemented method for trace-driven call-graph-aware proactive coordinated autoscaling of component microservices in an application includes generating performance-resource elasticity models of endpoints of the component microservices of the application. Workload levels of the endpoint of the component microservices is predicted based on user traffic observed at a front end service. A trace-level performance of the application is predicted for different microservice replica scaling based on the performance-resource elasticity models at end points, the ends points on the trace call graph and the predicted workload levels. A microservice replica scaling is recommended for each of the component microservices to meet predefined trace-level user service level objectives.

A computer-implemented method for trace-driven dependency-set-aware proactive coordinated autoscaling of component microservices in an application includes generating performance-resource elasticity models at a trace-level for traces of the application using dependency set of microservices for each trace. The method predicts workload levels of each of the traces, and also predicts a trace-level performance of the application for different microservice replica scaling based on the dependency set of microservices for each trace, performance-resource elasticity models and the predicted workload levels. The method uses distributed computing to recommend a microservice replica scaling for each of the component microservices to meet one or more predefined trace-level user service level objectives.

The invention relates to the technical field of cloud computing resource management, and discloses a storage and computing resource dynamic recombination management method, system, device and medium, which comprises the following steps: constructing a hierarchical management architecture comprising a local control center and a global network control center, constructing a uniform resource topology view of the whole network through a heartbeat mechanism and metadata reporting, automatic registration and state monitoring of calculation and storage resources are realized; multi-target task scheduling is carried out by adopting an improved genetic algorithm fused with a greedy strategy, and on the premise of meeting service quality constraints, localization processing of calculation tasks is preferentially realized to reduce network overhead; an elastic telescoping mechanism based on an ARIMA-LSTM mixed time sequence prediction model is introduced, and through deep learning of historical load data, a load peak value is pre-judged in advance and the scale of a resource pool is dynamically adjusted. According to the method, the global utilization rate of heterogeneous resources is effectively improved, and lossless rapid expansion and dynamic recombination of storage and calculation resources are realized.

The invention provides an intelligent scheduling management system based on a carbon footprint model, and the system achieves the dynamic coupling of calculation task scheduling and the real-time carbon intensity of a power grid through the cooperation of a carbon footprint modeling engine, a dynamic scheduling controller, a resource state sensing layer and a feedback execution module. The problem that a traditional data center resource scheduling system is lack of carbon sensing capacity is effectively solved. By establishing the dynamic association between the power source and the geographic position and fusing the multi-dimensional equipment energy consumption characteristics and the task demand labels, the scheduling decision automatically responds to the fluctuation of the regional renewable energy sources, and the overall carbon footprint is remarkably reduced on the premise of guaranteeing the calculation performance and the service level agreement. Meanwhile, the improved multi-objective optimization algorithm can efficiently search for an optimal balance point in a solution space formed by carbon emission, default rate and economic cost, the resource energy efficiency utilization rate is further improved through an elastic expansion mechanism based on real-time state perception, and finally unification of data center operation environment benefits and economic benefits is achieved.

The invention discloses a cloud computing resource elastic scaling method and system based on a Kubernetes cluster, and belongs to the technical field of artificial intelligence, S10, deploying multiple nodes and multiple Pods in the Kubernetes cluster for processing loads, and collecting load information of the cluster and the Pods in real time through a monitoring module; s20, dividing the collected load data into a training set and a test set according to a time sequence, and performing normalization processing; s30, establishing a load prediction model based on historical load data, and predicting a future load by using a frequency domain feature extraction and time-frequency fusion technology; s40, inputting the prediction model according to the real-time load data, and triggering a capacity expansion and contraction operation in combination with a set capacity expansion and contraction threshold value; s50, calling the Kubernetes AP I to execute the capacity expansion or capacity shrinkage operation of the Pod according to the predicted load and the capacity expansion and shrinkage decision; the method has the beneficial effects that the resource management can be optimized, the cost can be reduced, the cloud service can be ensured to keep stable and efficient operation in a complex and changeable business scene, and the service quality and competitiveness of an enterprise are improved.

The invention provides a large model reasoning task processing system and method and a medium, an event encapsulation module encapsulates reasoning task parameters into structured events through a preset event encapsulation rule and stores the structured events into a distributed message queue, asynchronous decoupling of task requests and model instance processing is achieved, and the efficiency of reasoning task processing is improved. And the task concurrent response efficiency is improved. Secondly, the event processing module dynamically allocates matched model instances according to reasoning events in the message queue, resources are called in real time based on event loads, the allocation mode of whole card monopolization or fixed quota of a static resource pool is changed, and the problems of resource fragmentation and scrambling are avoided. And finally, the resource prediction module analyzes historical resource consumption data, event resource demand characteristics and resource consumption data of the model instance, pre-judges the resource demand of the next stage in advance and formulates a resource allocation strategy, so that the computing resources can be elastically expanded and contracted according to the actual demand, and the utilization rate of the computing resources is effectively improved.

The invention relates to the technical field of artificial intelligence, and discloses an AI computing power platform based on an elastic scaling mechanism, comprising: a task queue module maintaining a dynamic task list composed of user tasks submitted by a plurality of user sides; the historical operation log database collects historical task log data of user task multi-dimensional resources; the task resource prediction module predicts a multi-dimensional resource demand according to the historical operation log data; the AI computing power scheduling module deploys the tasks to server nodes capable of meeting resource requirements by adopting a preset AI computing power resource scaling scheduling algorithm according to the task resource prediction result; and the state synchronization feedback module updates the task execution state and the cluster resource state and feeds back the state change. According to the method, the problem of balancing the resource utilization rate and the task performance is solved, the intelligence and the efficiency of AI task scheduling in a large-scale cluster environment are improved, the resource management requirements of real-time and high-concurrency tasks are met, and a better resource scheduling solution is provided for AI application.

The invention discloses a method, a system and equipment for processing massive time scale measurement data of an elastically telescopic power grid regulation and control system, and a storage medium, and the method comprises the steps: dynamically distributing the time scale measurement data to a plurality of fragments in a Hash ring space, and maintaining the load balance; for the time scale measurement data processing process of each fragment sent by the front acquisition subsystem to the SCADA subsystem, monitoring the data accumulation value of each level through a three-level cache feedback mechanism, and then triggering the elastic expansion and contraction of the fragment number according to a threshold value; performing time scale measurement processing on each fragment, recalculating a complete abnormal measurement correction through the SCADA subsystem, and synchronously writing into a real-time library section and a time sequence library cluster through double paths; and aggregating time sequence library cluster data through a time scale self-alignment engine, and generating and externally providing a millisecond-level whole-network same-time measurement section. According to the method, the problem of hotspot overload caused by traditional architecture fragmentation static distribution is solved, the peak throughput is 1 million / second, and high-precision time mark alignment data support is provided for power grid advanced application.

An autoscaling method and apparatus in a Kubernetes cluster, which method and apparatus are applied to the technical field of computers. The method comprises: in historical monitoring data, using an interpolation method to impute missing data between adjacent data points to obtain preprocessed sequence data; performing a fast discrete Fourier transform on the preprocessed sequence data to obtain a spectrogram of original data; analyzing the spectrogram, and identifying candidate periods in the spectrogram; using a preset correlation coefficient calculation function to verify the candidate periods to obtain a dominant period; on the basis of the dominant period and operation data in a cluster, performing peak prediction; and when a predicted peak exceeds a preset autoscaling threshold, triggering a scale-up or scale-down operation corresponding to a system. By means of the present disclosure, the requirement for resources in a period of time in the future can be predicted, and scaling up or scaling down can be performed, so that a more active and accurate resource allocation policy is realized, and the stability of a service is ensured.

The invention relates to a computing power resource scheduling method of an intelligent ship, and belongs to the technical field of machine learning. The method comprises the following steps: collecting computing power resource data of shipborne computing equipment of an intelligent ship in real time; predicting the computing power resource demand of each task based on the computing power resource data and the computing tasks; constructing a shipborne computing power resource scheduling model by taking the minimum task execution cost as a target and taking the task completion period as a constraint; constructing a Markov decision process, and solving the shipborne computing power resource scheduling model by using a multi-agent deep reinforcement learning algorithm based on the computing power resource demand to obtain an optimal scheduling strategy; and allocating computing power resources based on the optimal scheduling strategy. According to the method, under the condition that the total amount of shipborne computing power resources is limited, more shipborne task instances are served, elastic expansion and contraction of the resources are achieved, and an efficient and self-adaptive computing power resource management scheme is provided for the intelligent ship.

Certain aspects of the disclosure provide techniques for predictive autoscaling. A method includes determining resource utilization metrics for a plurality of instances of a service running in a container-based cluster for a plurality of timestamps over a period of time; applying a smoothing filter to the resource utilization metrics to obtain smoothed resource utilization metrics; adjusting each of the smoothed resource utilization metrics by a nominal value; calculating a plurality of ratio metrics for the smoothed resource utilization metrics; processing, with a machine learning (ML) model trained to perform resource utilization forecasting, the plurality of ratio metrics and to predict a future ratio metric for the service after a prediction time window; determining a future resource utilization for the service after the prediction time window based on the future ratio metric; and automatically adjusting configuration parameter(s) to modify a state of the container-based cluster based on the future resource utilization.

The invention provides a multi-virtual machine isolation operation system based on security level automatic scheduling, and relates to the technical field of cloud computing and virtualization. According to the system, an intelligent security level evaluation module is combined with multi-dimensional analysis to generate a task security level, a security-layered virtual machine resource pool realizes hierarchical cluster management and elastic scaling, an intelligent scheduling engine realizes task allocation based on security level matching and resource optimization, and a cross-level data exchange control module realizes fine-grained data management and control. The system solves the problems that an existing virtual machine management system is insufficient in security isolation, low in manual scheduling efficiency, poor in dynamic adaptability and weak in cross-domain data management and control, deep binding of the security level and resource allocation is achieved, and the resource utilization rate and the system robustness are improved. Performing subject term cloud computing; a virtualization technology; a security level; isolating the virtual machine; and automatic scheduling.

This invention discloses a method and apparatus for implementing a high-availability cloud firewall cluster, relating to the field of cloud firewall technology. The method includes session-based traffic splitting of data packets using a Session Flow Table (SLB); simplified processing of data streams for ALG-related protocols; and real-time monitoring of the health status and concurrent session count of each firewall node via SLB to achieve dynamic scaling of firewall nodes. This invention supports automatic elastic scaling of cloud firewalls, as well as multi-active high-availability clusters for NAT and ALG scenarios, while avoiding hot session standby between cluster nodes.

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 invention relates to the technical field of financial data intelligence, and discloses a financial personalized management classification system based on digital economy. According to the system, a dynamic mapping network is formed through weaving by constructing a user composite intention profile. And injecting a probe into a network node, performing compliance deconstruction and verification on a response sequence, driving behavior partition structure recombination, and performing re-calibration on the network according to the behavior partition structure recombination. And capturing an intention drift trajectory through a real-time data stream, dynamically and elastically telescoping network link weight, and finally fusing a strategy to complete management classification. According to the system, online driving optimization of data partitions in a compliance state and real-time dynamic shaping of a decision network by intention drift are realized, a closed-loop adaptive mechanism from bottom data to upper rules is formed, and classification accuracy, timeliness and compliance adaptability of financial personalized management in a dynamic environment are improved.

The invention provides a method for optimizing streaming DAG engine task operation based on K3S. The independent namespace and Pod resource group are created for each task, so that strong isolation among the tasks is realized, resource competition and mutual interference among multiple tasks are avoided, and the operation stability of key tasks is guaranteed. A K3S native Pod automatic expansion piece (HPA) is used for managing a resource group, so that the system can dynamically and elastically expand and contract according to a real-time load, the utilization rate of cluster resources is remarkably improved, and service fluctuation is effectively coped with; the operation and maintenance management is carried out by depending on a high availability (HA) mechanism built in the K3S, the operation and maintenance complexity of the cluster is greatly simplified, rapid fault detection and recovery can be realized without depending on external components such as ZooKeeper and the like, and the reliability and maintainability of the system are improved.

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 invention relates to the technical field of computers, in particular to a Kubernetes container vertical stretching adjustment method and device, and the method comprises the steps: collecting business data, system data and scene data; determining a message priority coefficient according to the message delay requirement, and quantizing and generating a load index according to the message priority coefficient, the request queue length and the push error rate; diagnosing a performance state according to the average push delay, the memory page change rate and the network bandwidth utilization rate; constructing a resource demand index by combining the load index, the performance state and the active connection number, and judging a resource demand state according to the resource demand index; and determining a scaling direction based on the CPU utilization rate, the memory utilization rate and the network bandwidth utilization rate, and determining a CPU adjustment amount and a memory adjustment amount in combination with the resource demand state, the scaling direction and the context switching times of the process in the container. According to the method, the utilization efficiency of cluster resources is maximized, and a higher-level intelligent solution is provided for elastic scaling of cloud native applications.

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 invention relates to the technical field of artificial intelligence security and cloud computing resource management, and discloses an AI reasoning service elastic scaling method and system for an edge cluster, and the method comprises the steps: constructing a dynamic risk file for each hardware computing unit in a computing cluster; distributing the reasoning service request to a target hardware calculation unit of which the risk state is matched with the security demand level; instantiating an independent logic calculation container for each allocated reasoning service request, and destroying the logic calculation container after the reasoning task is executed; and monitoring the resource configuration change of the computing cluster, and updating the risk state of the computing cluster in the dynamic risk file. According to the method, a mixed protection mechanism with logic isolation as a main part and physical reset as an auxiliary part is adopted, and hardware-level state pollution attacks triggered by antagonistic input are effectively defended in a dynamic elastic edge AI reasoning scene on the premise that the elastic scalability is not obviously sacrificed.

The application provides a cloud resource dynamic scheduling method and system based on virtualization technology, comprising: collecting the running state of each resource node in the cloud platform and extracting multi-dimensional time sequence features; constructing a resource topology intention perception model based on a graph neural network, identifying the load dependency relationship and potential bottleneck intention between resource nodes; taking the load dependency relationship and bottleneck intention as input, combining a reinforcement learning strategy network, and pre-generating a resource migration and elastic scaling decision sequence for a future time window before scheduling triggering; according to the decision sequence, performing hot migration or in-place elastic scaling operation on the target resource node, and updating the corresponding traffic forwarding rules in the virtual network topology; collecting the actual resource utilization rate and service quality indicators after scheduling execution, and feeding back to the reinforcement learning strategy network to optimize the feature weight of the intention perception model and the generation strategy of the scheduling decision. The application can improve the scheduling timeliness and resource utilization efficiency.

The invention provides a cross-node hybrid deployment method based on a declarative arrangement technology, which belongs to the crossing field of an embedded system and a virtualization technology, and comprises the following steps: expanding and defining a resource isolation area based on a CRD mechanism, tuning the resource isolation area based on a declarative API and a control loop model, creating a Daemon Set form agent management and control resource isolation domain, by expanding a self-defined resource mechanism, a resource isolation domain is managed and dynamically allocated as an object same as a K8s native resource container group, a user can create, schedule and manage the isolation domain and various tasks running in the isolation domain across equipment nodes through cluster control center service, and on-demand access and elastic expansion are supported.

The invention discloses a resource capacity expansion and contraction method and system, and the method comprises the steps: predicting resources needed in a next first time interval, and obtaining a predicted resource demand number; according to the current resource usage amount and the predicted resource demand amount, calculating a target capacity expansion amount or a target capacity reduction amount of the resources; and executing a capacity expansion and contraction task according to the capacity expansion and contraction strategy. According to the method, resource demands are accurately predicted based on various data, automatic elastic expansion of resources is realized, the resource utilization rate is effectively improved, and manpower is thoroughly liberated. And a gradual capacity expansion and contraction strategy is adopted, so that resources are saved, the quantity of the resources is smoothly changed, service jitter is avoided, and the stability of the service is guaranteed.

The invention provides a distributed identification generation method and system and a distributed service cluster, and relates to the field of distributed cluster processing, the method combines distributed member management based on a Gossip protocol with a Hash ring, and on the premise of ensuring that the generated snowflake identification is globally unique, gradually increased in trend and high in performance, the distributed service cluster is generated. Decentralization and automatic process management of the distributed service cluster on the Worker ID are realized, strong dependence on external coordination service is eliminated, and reliability, expandability and elastic expansion and contraction capability of the distributed service cluster 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.