209 results about "Autoscaling" patented technology

The invention discloses a computing resource scheduling method based on user demands and task priorities, which relates to the technical field of resource scheduling, and comprises the following steps: receiving a computing task request submitted by a user, analyzing and verifying explicit demand parameters and implicit demand parameters, and generating a standardized demand description object; acquiring cluster state data and external environment parameters in real time, constructing a user-task-environment three-dimensional feature tensor, and outputting a standardized feature vector group; and collecting a performance data flow of the container instance group, triggering an elastic scaling decision based on a pre-trained LSTM prediction model, dynamically adjusting cluster resource configuration and executing abnormal task rescheduling. According to the method, a user-task-environment three-dimensional feature tensor is constructed, and a dynamic mixed weighted priority score is generated in combination with a reinforcement learning model, so that space alignment and time sequence cumulative effect fusion of multi-dimensional features is realized.

The invention discloses a PCFarm resource scheduling method and system based on dynamic load prediction, and relates to the technical field of resource scheduling, and the method comprises the steps: constructing a feature extractor based on multi-modal feature fusion, mapping original data into a high-dimensional feature vector, and obtaining a dependency relationship between tasks; modeling a cluster topology, predicting a load propagation effect between nodes, constructing a spatio-temporal joint prediction framework, and fusing a time sequence and spatial topology information; proposing a multi-objective optimization function; training a scheduling strategy generator, and generating an optimal scheduling scheme based on the current cluster state; designing the elastic expansion and contraction of the prediction drive; constructing a migration cost model, and quantifying the influence of task migration on the performance; the overall load balance degree of the cluster is calculated through the global controller, and a coarse-grained migration instruction is generated. By arranging the load prediction module and the resource scheduling module, resource allocation is dynamically adjusted according to the real-time state and task requirements of the cluster, and a better resource scheduling effect is achieved.

The invention relates to the technical field of cloud platform monitoring analysis, in particular to a cloud platform computing power resource performance monitoring and real-time scheduling optimization method, which comprises the following steps of: acquiring real-time performance data of heterogeneous computing nodes; performing multi-dimensional index fusion on the real-time performance data to generate a fusion performance index set containing time correlation features; constructing a dynamic computing power topological graph based on the fusion performance index set; generating an elastic scaling strategy set according to the dynamic computing power topological graph; mapping a to-be-allocated task to a physical node meeting the topology constraint condition; and carrying out topology conflict detection on the scheduling result through a feedback verification module, and dynamically correcting the parameter weight of the elastic scaling strategy set. According to the method, resource conflicts are reduced, the utilization efficiency of computing power resources is improved, and the system bottleneck problem caused by topology conflicts is avoided, so that the stability and the load balancing capacity of the system are enhanced.

The invention discloses an agent platform resource management method and device based on a cloud native architecture and a medium, and the method comprises the steps: packaging an agent application into an independent container instance based on a containerization technology, and deploying the container instance to a target node; acquiring task demand information of the intelligent agent in real time, and generating a dynamic scheduling scheme by combining the resource state data and through a multi-target optimization algorithm so as to allocate the task to a target container instance; according to a matching function of the capability vector of the intelligent agent and the task demand vector, calculating the integrating degree of the intelligent agent and the task so as to generate a collaborative decision-making result and issue the collaborative decision-making result to the target intelligent agent; the resource utilization rate and the task execution state of the intelligent agent are monitored, an elastic telescoping mechanism or task rescheduling is triggered according to feedback data monitored in real time, and a resource allocation strategy is dynamically adjusted.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization 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 discloses an elastic management and optimal scheduling method and system for cloud computing resources, and belongs to the technical field of cloud computing resource management.The method comprises the steps that a cluster composed of multiple types of intelligent agents is constructed, and the intelligent agents comprise a resource evaluation intelligent agent, an elastic telescopic intelligent agent, a load balancing intelligent agent and a fault recovery intelligent agent; the intelligent agents are distributed and deployed on different nodes of a cloud computing data center, and information interaction is realized through a communication interface based on RESTful API and a distributed message bus constructed by a gRPC protocol; a unified agent management platform is set up and is responsible for registration, monitoring, scheduling and dynamic adjustment of agents, and orderly operation and efficient cooperation of the whole multi-agent system are ensured. According to the method, the functions of efficient elastic allocation, intelligent load balancing, rapid fault recovery, continuous optimization evolution and the like of resources in the cloud computing data center can be realized, and the quality and competitiveness of cloud computing services are effectively improved.

Methods and apparatuses are described herein for wireless transmit / receive unit (WTRU)-based edge compute autoscaling. For example, a WTRU may receive a message from a network device. The message may include a list of one or more edge application servers (EAS) associated with an un-instantiated application. The WTRU may select an EAS from the list of one or more EAS associated with the un-instantiated application. The WTRU may transmit a request to an edge data network (EDN), the request indicating a request to instantiate the un-instantiated application at the selected EAS. The WTRU may receive a response from the EDN, the response indicating that the un-instantiated application has been instantiated at the selected edge application server and indicating information for accessing the now instantiated application at the EAS.

The invention provides an AIGC high-availability elastic computing system and method, which are used for solving the problems of high concurrency, low resource utilization rate, poor expandability and the like faced by AIGC service. The system comprises an API server, message middleware, a plurality of computing nodes, a monitoring system and a management system. And the computing node actively pulls the task from the task queue according to the processing capacity of the computing node, selects proper computing power resources after analyzing the demand, and executes the AIGC task in the containerization environment. The monitoring system tracks the state of each component in real time, and the management system dynamically adjusts resource configuration according to monitoring data. The method has the advantages that the concurrent processing capacity and the resource utilization rate of the system are improved, elastic stretching and intelligent operation and maintenance are achieved, the fault-tolerant capacity of the system is enhanced, meanwhile, the operation cost is reduced, and an efficient, reliable and economical service platform is provided for AIGC application scenes of various scales.

The invention discloses a multi-cluster heterogeneous computing power scheduling method oriented to an AI training reasoning task, and belongs to the technical field of computers. According to a scheduling mechanism with issuing of the AI task as a core, the state of a resource pool is monitored in real time, resources are dynamically allocated, it is ensured that the AI task is efficiently executed, the use efficiency of the whole resource pool is improved, and the load state of an AI application example is detected in real time; through an elastic telescoping function, AI application examples are automatically expanded and shrunk, stable operation of AI tasks is ensured, resources of a plurality of clusters are integrated into a unified resource pool, centralized management and cooperative scheduling of the resources are achieved, the load condition of each cluster is monitored in real time, load balancing is automatically carried out among the clusters, and the AI tasks are reasonably distributed to different clusters. And the GPU resources of the specified model are accurately allocated to the AI task according to the actual demand of the AI task, so that the situation that the AI task application runs on non-optimal resources is avoided, and the efficient running of the AI task is ensured.

Disclosed herein are system, method, and computer program product embodiments for autoscaling parameters of a computing environment. The methods include receiving a system metric that relates to usage of a computing resource associated with an application executed within the computing environment, and determining whether the system metric is within a desired operating range. When the system metric is determined to be not within the desired operating range, the methods also include determining a scaling rule for autoscaling one or more parameters of the computing environment based on a number of currently utilized computing resources, and autoscaling the one or more parameters of the computing environment to bring the system metric within the desired operating range.

The invention relates to the technical field of cloud computing, and discloses a multi-tenant cloud native database horizontal extension prediction method, which comprises the following steps of: A, collecting and preprocessing multi-channel data, collecting working load data of database instances, and preprocessing the collected original data; step B, resource load prediction: a Transform network is adopted to form a resource load prediction model, the Transform network predicts the resource load, and a prediction result is output; and step C, performing horizontal elastic expansion scheduling, optimizing a K8s HPA strategy based on a prediction result, and realizing automatic expansion and capacity reduction of a model reasoning service instance. According to the method, the number of database instances is adjusted based on the demand prediction result, it is guaranteed that resource supply is within a reasonable range, and hysteresis of elastic expansion and resource waste are avoided. The invention further discloses a multi-tenant cloud native database horizontal extension prediction system and device and a medium.

The invention discloses a method and a device for supporting elastic expansion capability by computing power resources of an intelligent computing center. The method comprises the following steps: monitoring a QPS index of a Kubernetes cluster service in real time, and dynamically dividing service state grades after smoothing data through a sliding window; dynamically adjusting a QPS threshold value based on the historical data and the cluster load; the HF service dynamically expands and shrinks the capacity according to the QPS, the MF service keeps a single instance, and the LF service releases the instance and pre-caches resources to the SSD node; the service life cycle is managed through queue state transition, and cache is optimized in combination with an LRU-LFU mixed elimination strategy and a disk pressure trigger mechanism; the device comprises a monitor, a scheduler, a router, a three-level cache system, a fault-tolerant processing unit, a processor and a memory. And a three-level cache system is adopted to accelerate cold start, so that the fault recovery time is effectively shortened, and the problems of high loading delay, low resource utilization rate and cross-node scheduling of the AI large model are solved.

The invention relates to a micro-service capacity expansion and contraction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring load monitoring data of a micro-service architecture application; carrying out filtering processing on the load monitoring data through a preset load elastic expansion rule to obtain rule filtering data; inputting the rule filtering data into a preset load prediction model to carry out load prediction processing to obtain an average load change trend index of the micro-service architecture application; carrying out diffusion capacity judgment processing to obtain a capacity expansion and contraction mode and diffusion capacity parameters; and the micro-service architecture application is subjected to capacity expansion and shrinkage processing. According to the method, capacity expansion and shrinkage are judged and processed according to the average load change trend index, and rapid and accurate service deployment and adjustment can be realized according to the real-time service load and the resource use condition, so that the expansibility of the micro-service architecture application is improved, and the availability of an application system is ensured.

The invention discloses a container cloud elastic expansion and contraction method, and particularly relates to a container cloud elastic expansion and contraction method based on load prediction, which comprises the following steps: S1, data acquisition and storage; s2, data preprocessing and rule filtering; s3, training a load prediction model; s4, load prediction and trend analysis; s5, capacity expansion and shrinkage judgment and parameter calculation are carried out; and S6, executing a capacity expansion and contraction strategy and performing real-time adjustment. Compared with a traditional load prediction method, the method has the advantages that the deep learning model based on the PatchMixer is adopted, the time sequence characteristics of the cloud resource load can be more accurately captured, and the method has obvious advantages especially when nonlinear and non-stationary data are processed. Through high-precision load prediction, reliable input data can be better provided for elastic expansion and contraction of the container cloud, and resource adjustment timeliness and prediction accuracy are remarkably improved.

The invention discloses a large language model reasoning method based on a distributed KV cache pool. According to the method, a platform receives requests which are sent by a large number of users and need a large language model service, the requests of the users and machines in a cluster are modeled correspondingly, and then corresponding strategies are used for processing. In addition, the use condition of machine resources in the cluster is also considered, and machines with more idle resources are preferentially considered. In this way, interference caused by resource competition is reduced to a certain extent. Meanwhile, by abstracting memories of numerous NPU cards into a distributed KV cache pool, elastic expansion and contraction are facilitated during request processing. Through the method, an efficient cluster based on a large language model can be constructed, and a corresponding platform can better understand the demands and intentions of users so as to provide more timely and personalized services.

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 cloud computing, in particular to a container cloud elastic scaling method based on a GRU-attention mechanism, and the method comprises the following steps: S1, constructing a GRU-attention mixed model, and generating a load prediction result through the GRU-attention mixed model; s2, based on the load prediction result generated in the step a), through an improved HPA mechanism, calculating the number of Pods required in a period of time in the future, and executing a corresponding capacity expansion and contraction strategy; and S3, continuously analyzing resource supply indexes through a resource supply evaluation module, and optimizing capacity expansion and shrinkage decision parameters according to quantitative results of an insufficient supply rate and an excessive supply rate so as to improve resource utilization efficiency. According to the invention, by optimizing resource allocation and reducing resource waste, the operation cost of the container cloud environment is effectively reduced, and especially in a large-scale and high-dynamic load scene, the method has remarkable economic benefits.

The invention relates to the technical field of computing power infrastructure, and provides a method and device for supporting elastic expansion and contraction capability of computing power resources of an intelligent computing center, and the method comprises the steps: S1, monitoring the calling frequency of a target service; s2, according to the calling frequency, the current service level of the target service is judged, and the service level comprises a high-frequency service, an intermediate-frequency service, a low-frequency service and a cache-free service; and S3, running the target service according to the service level to which the target service belongs currently. Therefore, the intelligent computing center can dynamically adjust the allocation of the computing power resources according to the actual demand of the service, realizes the elastic expansion and contraction capability of the computing power resources, avoids the starting delay of the service, improves the user experience, avoids the waste of the computing power resources, improves the utilization rate of the computing power resources of the intelligent computing center, and improves the user experience. And the performance of the high-performance computing power resource of the intelligent computing center is improved.

The invention discloses a cloud computing resource scheduling method and system based on an elastic telescopic double-layer scheduling framework, and relates to the field of cloud computing resource scheduling. The double-layer scheduling framework comprises a task allocation scheduler and a virtual machine automatic telescopic scheduler; the task allocation scheduler determines the state, action and reward function of the first agent by collecting the task characteristics of the current cloud computing platform and the use data of the virtual machine; the virtual machine auto-scaling scheduler determines the state, action and reward function of the second agent by collecting the workload and resource utilization rate data of the current cloud computing platform; and solving a task allocation decision of the first agent by adopting a double-layer scheduling algorithm, and solving a virtual machine adjustment decision of the second agent by adopting the double-layer scheduling algorithm. An elastic telescopic double-layer scheduling framework based on deep reinforcement learning is innovatively proposed, and efficient task scheduling and elastic resource allocation are realized.

The invention discloses a method and device for elastically and telescopically supporting NodeManager resource scheduling of a big data cluster. The method comprises the steps that S01, host load evaluation and resource management are carried out, and whether the load of a host is excessive or not is judged; s02, according to a NodeManager resource optimization strategy, dynamic optimization of resources is carried out; s03, automatic management of NodeManager resources: according to the data obtained in the step S01, determining whether the resource allocation of the NodeManager needs to be adjusted or not; and S04, identifying and processing NodeManager resource saturation, and discovering a NodeManager machine with resource saturation in time by continuously monitoring a cluster state. According to the method and the device for elastically and telescopically supporting the NodeManager resource scheduling of the big data cluster, the NodeManager resource scheduling of the big data cluster can support automatic elastic telescoping through the technical means of improving the NodeManager resource utilization rate of a single physical machine, controlling the upper limit of the NodeManager resource of an over-configured CPU and a memory, discovering and responding to a saturated machine, expelling running tasks and the like.

The invention belongs to the technical field of resource elastic scaling, and discloses an efficient execution method and system for a computing task in a multi-mode intelligent computing network, and the method comprises the steps: collecting the resource use time sequence data of the task in real time, wherein the resource use time sequence data comprises a current time step length and T-1 historical time step lengths adjacent to the current time step length; inputting the time sequence data into a trained resource demand prediction model to obtain resource use time sequence data in L prediction time steps; wherein L is the time window length of the task load stability determined by the resource volatility and modal characteristics of the task; determining the optimal resource configuration required by the task in the L prediction time steps by using the resource use time sequence data in the L prediction time steps; and comparing the optimal resource configuration with a preset elastic scaling strategy rule, and determining whether resource elastic scaling needs to be carried out or not. Furthermore, the invention further provides a resource recommendation mode for a new task, and the resource allocation accuracy and stability and the response speed of the system can be improved.

The invention discloses a load-aware multi-micro-service replica elastic scaling method in a computing power network, and aims to cope with a dynamic online working load environment, ensure that end-to-end response time delay meets a service level agreement (SLA) and improve the utilization rate of micro-service resources at the same time. According to the method, firstly, historical performance data of all micro-services in a Kubernetes cluster are collected through plug-ins such as Prometheus, cAdvisor and Istio, the data are stored in a time sequence database, and a training data set is constructed; then, a graph data structure is constructed according to the calling relation between the micro-services and the micro-service features in the data set, the multi-micro-service CPU utilization rate and a P90 response delay predictor are trained through a graph attention network, and therefore an intelligent agent interactive simulation environment is formed. Based on the simulation environment, a deep context multi-arm bandit model is designed and trained, and an optimal copy adjustment strategy is selected in a large state space and a complex nonlinear relation in combination with an upper confidence bound (UCB) strategy, so that automatic horizontal stretching of the multi-micro service system is realized. And finally, deploying the trained model in an actual environment, regularly monitoring and updating context information of the micro-service through Prometheus, performing real-time copy adjustment, reducing an SLA violation risk and improving a resource utilization rate.

The invention discloses an array sensing marginal container elastic expansion and contraction method, which comprises the following steps of: obtaining specific resource information of a container in a Pod replica set load of an aggregation container in a pre-constructed substation multi-container cluster marginal cloud framework; calculating load data of the Pod replica set according to the specific resource information of the container; calculating the optimal prediction time of all Pod replica sets generated by the container; according to historical load data formed by the load data of the Pod replica set and the optimal prediction time of the Pod replica set, predicting a Pod replica set load prediction value of the next stage; according to a Pod replica set load prediction value of the next stage, calculating an expected replica number; and controlling the capacity expansion or the capacity shrinkage of the container according to the expected copy number at the time node needing the capacity expansion or the capacity shrinkage. According to the method, capacity expansion can be completed before a load peak arrives, so that response delay caused by a Pod initialization process is avoided, and it is guaranteed that the load of the replica set is moderately in a target state; the invention further discloses an array sensing edge container elastic stretching system.

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.

The invention discloses an elastic expansion strategy making method and device, equipment, a medium and a product. The method comprises the following steps: acquiring at least one load-time change curve of a target server in a sampling time period, and training a preset machine learning model by using each load-time change curve to obtain a load prediction model; obtaining a performance index curve set of a target server, and calculating according to the performance index curve set to obtain a load upper limit threshold value and a load lower limit threshold value of the target server; and obtaining a predicted load curve of a target time period through the load prediction model, and obtaining an elastic scaling strategy matched with the target server according to the predicted load curve, a load upper limit threshold value and a load lower limit threshold value. Through the technical scheme of the invention, the formulation of the elastic scaling strategy for the server load can be realized, the formulation efficiency of the elastic scaling strategy is improved, and the adjustment efficiency of the server load is further improved.

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.

Techniques described herein can optimize usage of computing resources in a data system. Dynamic throttling can be performed locally on a computing resource in the foreground and autoscaling can be performed in a centralized fashion in the background. Dynamic throttling can lower the load without overshooting while minimizing oscillation and reducing the throttle quickly. Autoscaling may involve scaling in or out the number of computing resources in a cluster as well as scaling up or down the type of computing resources to handle different types of situations.

The invention belongs to the technical field of cloud computing resource management, and provides a service elastic scaling system based on resource calling, which comprises a resource calling analysis module used for constructing a dynamic resource dependency graph by monitoring calling relations of service components in an application program, and analyzing the resource calling analysis module; analyzing historical calling data in the dynamic resource dependence graph by using a long-short-term memory network model, and predicting a future resource demand condition; the elastic scaling strategy generation module is used for dynamically generating a priority queue of resource calling and generating an elastic scaling strategy based on the topological structure and the real-time load data of each service component; and the elastic scaling implementation module is used for implementing service elastic scaling through resource buffering and fusing of resource calling abnormity according to the elastic scaling strategy. According to the method, the application program resource calling link is analyzed, the machine learning prediction and the security policy are combined to dynamically optimize the scaling decision, the calculation overhead caused by frequent starting and stopping of resources can be reduced, and the data security and the service stability are improved.