149 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 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.

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.

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 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 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 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 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.

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.

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 present disclosure describes a system, method, and computer program for predictive autoscaling for faster searches of event logs in a cybersecurity system. In one embodiment, the system receives search-related signals from a plurality of signal sources. The signals are indicative of: (1) a user's intent to perform a search for event logs in a cybersecurity database, (2) how computationally intensive the potential search is likely to be, and (3) the currently available computational resources. The signals are evaluated, and an autoscale prediction score is calculated. The autoscale prediction score reflects the likelihood of a user to submit search, the computational resources required for the potential search, and the currently available computational resources. The system scales computational resources in accordance with the autoscale prediction score. These steps are performed before any search is submitted by the user in a search user interface.

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 provides an implementation method and device for application atomization fusion orchestration based on OPENAPI, and relates to the technical field of computer application, and the method comprises the steps of service disassembly and interface definition, service registration and discovery, orchestration and adaptive execution, data processing and mapping, security management and control, and integrated deployment and operation and maintenance. According to the invention, by splitting complex services into atomization services and defining interfaces according to OpenAPI specifications, service management is facilitated, the integration cost is reduced, and the flexibility and expansibility of the system are improved; by constructing a service registration center and combining a weighted polling algorithm, load balancing is realized, and efficient and stable calling of services is guaranteed; an API gateway, a containerization technology and an automatic operation and maintenance system are utilized to realize service rapid deployment, elastic expansion and intelligent operation and maintenance; meanwhile, data are processed through semantic analysis, format conversion and the like, and multi-level identity verification and encryption are adopted to guarantee safety.

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 present application discloses a data processing method, device, electronic device, and computer-readable medium, the method comprising: after creating an elastic scaling rule for a target cluster, first configuring a cache space corresponding to the target cluster according to the elastic scaling rule; then obtaining indicator detection status data of the load type rule item within a replenishment time range according to the cluster identifier of the target cluster and the indicator item identifier carried by the load type rule item in the elastic scaling rule, and storing the indicator detection status data in the cache space corresponding to the target cluster; subsequently, continuously obtaining the indicator detection update result corresponding to the target cluster during the polling interval by polling, and updating the storage content in the cache space corresponding to the target cluster according to the indicator detection update result, so that the purpose of elastic scaling decision for a cluster can be achieved by means of one-time replenishment + continuous polling and updating.

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.