46 results about "Workload prediction" patented technology

The invention discloses a data center energy consumption optimization method based on electricity price and load prediction, and relates to the technical field of scheduling optimization of a demand side, and the method comprises the steps: collecting parameters related to the energy consumption of a data center, constructing a data center energy consumption model based on workload transfer, proposing an electricity price and workload prediction model based on an LSTM neural network, and optimizing the energy consumption of the data center. The method comprises the following steps: constructing a data center load optimization scheduling decision model in a market environment by taking minimization of power purchase cost as an objective function and considering constraint conditions, and performing simulation verification on the model by utilizing an IEEE30 node system to obtain the electricity price of a prediction day, the arrived workload, the workload scheduling condition and the power purchase cost. According to the data center energy consumption optimization method based on the electricity price and load prediction, through introduction of the LSTM model, short-term and long-term fluctuation of the electricity price can be accurately predicted.

The invention relates to a method and a device for reducing response time delay of a deep learning model and a storage medium, which are applied to the technical field of artificial intelligence, and comprise the following steps: acquiring a historical load through sliding window sampling, acquiring initial load prediction data by using a dynamic joint prediction mechanism based on the historical load, obtaining an error compensation value through the corresponding actual load data sequence, and performing error compensation on the initial load prediction data to obtain final load prediction data; determining the total number of instances required for a period of time in the future based on the final load prediction data sequence; the number of instances which are currently operated is adjusted to match the total number of instances required in a period of time in the future, so that the response time delay of large-scale deep learning model reasoning work is reduced; through accurate workload prediction and resource scheduling, the response time delay of model reasoning can be effectively reduced, the achievement rate of a service level target is improved, and the service cost is reduced.

The present invention provides a method for optimizing cooling resources in a data center based on workload prediction and a system thereof. The method includes the steps of: continuously collecting data in real-time from a plurality of sensors distributed across various sections of the data center; predicting future workload demands and corresponding cooling requirements over time and space in each section of the data center using historical and real-time collected data; determining whether to allocate the predicted workload demand to a section that meets the corresponding cooling requirements or to adjust the cooling resources to achieve the required cooling levels, whichever results in more optimized energy usage; and dynamically adjusting the cooling resources in each section of the data center based on real-time and predicted workload demands over time to minimize energy consumption and operational costs, thereby ensuring optimal cooling without over-provisioning.

The invention provides a distributed computing power center load and energy consumption collaborative scheduling method and system based on artificial intelligence, a storage medium and electronic equipment, and relates to the technical field of load and energy consumption collaborative scheduling. According to the method, a computing power center node workload prediction model, a computing power center node workload energy consumption prediction model and a cooling system energy consumption prediction model are respectively constructed by utilizing GAT and LSTM, the three models cooperatively work through a cascade architecture, and the output of a preorder model is used as the input of a subsequent model, so that the data interaction capability among the models is enhanced; and the overall prediction performance is improved. Besides, the strong language generation and knowledge reasoning capabilities of the generative artificial intelligence are utilized to generate a flexible and efficient scheduling strategy, so that the flexibility and the intelligence degree of the system are remarkably improved, and the system can better adapt to complex and changeable computing power demand scenes.

The specification provides a workload prediction method, device, medium and computer system in a cloud computing service, the method comprising: in response to a first trigger event, obtaining first time series data corresponding to system state data in a first time period; based on the first time series data, performing a first prediction for a future first time domain range to obtain a first prediction result; in response to a second trigger event, obtaining second time series data corresponding to system state data in a second time period; based on the second time series data, performing a second prediction for a future second time domain range to obtain a second prediction result; evaluating the accuracy of the first prediction result to obtain an evaluation result; and determining a target prediction result of the workload according to the evaluation result, the first prediction result and the second prediction result.

The invention relates to the technical field of cloud computing, and provides a cloud resource workload prediction method based on multi-scale self-adaption in a computing network environment, and the method comprises the steps: carrying out the data collection of a workload sequence of a cloud data center through employing a sample division method based on a sliding window, and obtaining the cloud workload data of different time scales; carrying out noise reduction processing on the cloud working load data by adopting an adaptive threshold noise reduction method based on wavelet multi-scale decomposition, and carrying out Fourier transform to extract frequency domain features; carrying out sample division on the processed cloud working load data by adopting an adaptive mode clustering algorithm, and outputting a mode data set; adopting a Mixup data enhancement method based on mode driving to generate a mixed sample, adding the mixed sample into the mode data set, and constructing an enhanced mode data set; and obtaining a cloud resource workload prediction result by adopting a similarity alignment dynamic integrated prediction method. Finally, an ablation experiment proves that the method has higher prediction precision.

The invention provides a self-supervised multi-scale cloud workload prediction method and system in the technical field of cloud computing resource management. The method comprises the steps of S1, collecting time sequence data of workloads of physical machines in a cloud environment; s2, performing data enhancement operation on each time sequence data to obtain enhanced sequence data; s3, constructing a multi-scale encoder based on the multi-scale patch embedding layer and the scale sensing layer; s4, performing self-supervised training on the multi-scale encoder based on the enhanced sequence data; step S5, adding a prediction head in the multi-scale encoder, introducing an adapter with a specific scale, obtaining a preset number of annotation data, performing fine adjustment on the prediction head and adapter parameters of the adapter based on each annotation data, and then constructing a cloud workload prediction model; and S6, performing workload prediction based on the cloud workload prediction model. The method has the advantage that the workload prediction precision is greatly improved under the condition of scarcity of labeled data.

A method for incentivizing contact center agents with scheduling perks that includes: receiving a workload forecast and staffing plan for a current shift; receiving a service level target; receiving types of scheduling perks; performing a selection routine for determining a select scheduling perk for offering to a select agent; and sending a scheduling perk offer via electronic communication to the select agent. The selection routine may include: selecting a proposed scheduling perks and a proposed agent; modifying the staffing plan to create a proposed staffing plan that reflects the proposed scheduling perk; predicting a proposed forecasted target adherence using the modified staffing plan; determining whether the proposed forecasted target adherence satisfies a threshold defined by an acceptable forecasted target adherence and, if so, deeming the proposed scheduling perk as select scheduling perk and proposed agent as select agent.

The application provides a kind of self-supervision multi-scale cloud workload prediction method and system in the technical field of cloud computing resource management, method includes: step S1, the time series data of each physical machine workload in cloud environment is collected;Step S2, each time series data is subjected to data enhancement operation to obtain enhanced sequence data;Step S3, based on multi-scale patch embedding layer and scale perception layer, multi-scale encoder is constructed;Step S4, based on enhanced sequence data, the self-supervision training of multi-scale encoder is carried out;Step S5, in multi-scale encoder, prediction head is added, and the adapter of specific scale is introduced, obtain preset quantity of labeled data, based on each labeled data, the adapter parameters of prediction head and adapter are fine-tuned, and then cloud workload prediction model is constructed;Step S6, based on cloud workload prediction model, workload prediction is carried out.The application has the advantages that in the case of labeled data scarcity, the workload prediction accuracy is greatly improved.

The present application discloses a method, device and storage medium for quantifying the workload of an imaging report, including: obtaining a target imaging report corresponding to a patient, and extracting multiple first key features in the text content of the target imaging report using an NLP model; encoding each first key feature using a pre-set encoding rule, and determining multiple feature codes corresponding to each first key feature and multiple encoding values ​​corresponding to each feature code; generating a corresponding first data set based on multiple feature codes and multiple encoding values; and inputting the first data set into a pre-set workload prediction model, and determining the workload corresponding to the target imaging report. The technical effect of truly reflecting the workload of doctors when filling out diagnostic reports based on target imaging reports and enhancing the work enthusiasm of doctors is achieved.

The application discloses a server energy efficiency dynamic optimization method based on workload prediction and a deep learning model, relates to the cross field of server energy efficiency optimization and deep learning technology, and comprises the following steps: collecting server multi-source data, performing space-time alignment on the server multi-source data, extracting causal characteristics, constructing a standardized data set, constructing a time series graph convolution network, combining a multi-head attention mechanism, outputting a probabilistic load prediction interval, constructing an energy efficiency objective function based on a Lyapunov optimization framework, quantifying performance loss and temperature drift, and outputting multi-agent optimization constraints, defining a multi-reinforcement learning agent, outputting a collaborative adjustment instruction through a counterfactual baseline algorithm, constructing a digital twin shadow model to deduce energy efficiency, combining a meta-learning fine-tuning model, forming a closed-loop optimization of physical and digital double verification, realizing dynamic and accurate optimization of server energy efficiency, and adapting to multiple computing power scenes.

Techniques for multi-layered prediction of computing workloads are disclosed. A system identifies a level of granularity associated with a request for predicting the computing workload of a particular entity. The system obtains attribute data for computing resources at the identified level of granularity. The system determines whether computing resources not specified in the request should be included in the workload prediction. The system applies a time series prediction model to time series data obtained from the computing resources associated with the request. The system presents one or more workload predictions for the computing workload associated with the request.

The application relates to a cloud service workload prediction method and system based on a convolution enhanced Transformer, which comprises the following steps: collecting cloud server workload data and preprocessing the cloud server workload data to decompose the cloud server workload data into a trend component and a residual component; constructing a workload prediction model, training the workload prediction model by using the trend component and the residual component, and obtaining a trained workload prediction model; predicting the cloud server workload by using the trained workload prediction model to obtain a workload prediction value; predicting the trend component by using a trend information capturing module to obtain a prediction result of the trend component; predicting the residual component by using a convolution enhanced Transformer encoder module to obtain a prediction result of the residual component; and fusing the prediction result of the trend component and the prediction result of the residual component by using a feature fusion module to obtain a final workload prediction value.

The invention belongs to the technical field of intelligent traffic, and discloses a driver workload evaluation method based on a skin electric signal under various hearing interferences, which inspects various different driving scenes to research the comprehensive influence of navigation frequency and music rhythm on the driver workload, combines subjective evaluation with the skin electric signal by adopting a double-index method, and improves the driver workload evaluation accuracy. Strong and consistent correlation is shown between SCL distribution of the galvanic skin signals and subjective evaluation scores, a stable basis is provided for the galvanic skin signals serving as main physiological indexes of a workload prediction model, and driver workload prediction based on the galvanic skin signals under various auditory interferences is more accurate.

The invention discloses a database query pre-optimization method, system and device and a medium, belongs to the technical field of database and AI fusion, and aims to solve the technical problem of how to reduce database query optimization overhead and improve database query response performance. A workload prediction framework adapting to a dynamic environment is designed, logic plan cache management is optimized based on a prediction result, accurate perception and quick response to query modes and resource demand changes are realized, and database performance and expansion capability are comprehensively improved; the method specifically comprises the following steps: predicting a workload trend: performing semantic-based query template characterization and workload sequence prediction based on a multi-scale attention mechanism to realize prediction of a future arrival query SQL (Structured Query Language) of a current workload; and logic plan caching: effectively caching the logic plan of the query statement output in the workload sequence prediction stage based on the multi-scale attention mechanism.

The invention discloses a project progress optimization method and system based on data analysis, and relates to the technical field of project progress optimization, and the method comprises the steps: obtaining the plan information of a target building project, and constructing a first workload prediction model based on historical project data; and based on the activity and position data collected by the wearable device, monitoring the task execution state of each worker in real time, and constructing a second progress assessment. When it is detected that the overall progress deviates from the target threshold value, the task scheduling sequence and the distribution strategy of the remaining workload are dynamically adjusted, and closed-loop optimization control over the construction period is achieved. According to the project progress optimization method based on data analysis provided by the invention, the workload and progress can be evaluated more comprehensively by combining multi-dimensional information such as historical data, environmental factors and staff efficiency, the limitation of depending on a single data source or simple average is avoided, and the adaptability of a project is remarkably improved. And a multi-objective optimization algorithm and reinforcement learning are adopted.

To enable accurate determination of the work effort required to port large-scale software projects across disparate platforms.SOLUTION: A method of predicting porting work effort is provided. A total porting work effort to port a software project from a source platform to a disparate target platform is predicted using a first porting work effort, a second porting work effort, and a third porting work effort. The software project is ported from the source platform to the disparate target platform provided that the total porting work effort is less than a defined maximum porting work effort threshold level.SELECTED DRAWING: Figure 1

The application belongs to the technical field of intelligent transportation, and discloses a driver workload prediction method based on multiple physiological signals under multiple auditory disturbances, which is created by combining three music rhythms (none, slow and fast), two navigation modes (high frequency and low frequency) and three traffic scenes (regular road, school road and construction site road), collects multi-modal data including skin electricity signal EDA, electrocardiogram signal ECG, electroencephalogram signal EEG, driving behavior characteristics and subjective workload evaluation, and the data is used to build a workload prediction model, and a stacking model is adopted, and the stacking model integrates multiple machine learning models to improve the prediction performance.

This invention provides a method, apparatus, and medium for predicting serverless user workloads. The method includes: collecting historical data from a serverless architecture, wherein the historical data includes performance monitoring data as an independent variable and the number of jobs waiting to be executed as a dependent variable; extracting root cause features, time-division features, differential features, and interaction features based on the performance monitoring data and the number of jobs waiting to be executed; training a preset workload model using the performance monitoring data, root cause features, time-division features, differential features, and interaction features as a training dataset to obtain a trained workload model; and using the trained workload model to predict serverless user workloads. This method, apparatus, and medium can solve the problem that existing workload prediction methods, due to the use of single features for prediction, are prone to weak data feature extraction and large prediction deviations.

This application can be applied to fields such as resource allocation, fintech, and smart healthcare. It discloses a resource allocation method and apparatus, storage medium, and computer equipment based on dynamic control. The method includes: acquiring the resource budget plan and project execution data of the target project; calculating and predicting resource demand based on the project execution data using a preset project resource prediction model; if the resource budget plan covers the predicted resource demand, calculating a resource idle matrix and matrix score based on the resource budget plan and the predicted resource demand; determining a first budget adjustment plan for the resource budget plan based on the matrix score; if the resource budget plan does not cover the predicted resource demand, acquiring multiple project nodes of the target project and the expected targets of each project node; calculating the predicted workload value based on the expected node targets using a preset workload prediction model; and determining a second budget adjustment plan for the resource budget plan based on the predicted workload value of each project node.

The invention relates to the technical field of cloud work load prediction, and particularly provides a Kubernetes-based cloud work elastic telescopic capacity expansion method, a PM-SGRUs-DTW model is applied to a Kubernetes platform, the PM-SGRUs-DTW model comprises a dynamic integrated prediction key design of multi-scale time sequence decomposition and feature extraction, adaptive mode clustering and data enhancement and similarity alignment, and the dynamic integrated prediction key design of the multi-scale time sequence decomposition and feature extraction, adaptive mode clustering and data enhancement and similarity alignment is applied to the Kubernetes platform. And finally, performing a prediction experiment on the CPU utilization rate and the memory utilization rate based on the real data sets of Aliaba and Google. According to the method, the prediction result of the PM-SGRUs-DTW model is combined with the original HPA mechanism of Kubernetes, the capacity expansion and contraction decision in the initial stage of load fluctuation is realized by accurately predicting the load trend and pre-judging the resource demand, the response delay of an elastic expansion and contraction mechanism is reduced, and the resource utilization efficiency and the service quality are improved.

The invention discloses a distributed database buffer pool management system and method, belongs to the technical field of databases, and aims to solve the technical problem of how to combine AI prediction and a memory mapping mechanism to realize efficient database buffer pool management. Comprising a workload prediction module used for predicting the arrival condition of a query load of each user in a next time window through a prediction model, and sending each group of data fragment file lists to a data preloading module of a corresponding database node; the data preloading module is used for loading data required by the next time window from the virtual memory to the physical memory; the intelligent buffer pool management module is used for locking data required by a next time window according to the memory utilization rate on the basis of an LRU cache elimination strategy of an operating system, and adaptively adjusting the size of the time window and parameters of a prediction model; and the dynamic adaptation and feedback optimization module is used for performing parameter optimization on the prediction model and unlocking the data fragment file.

The invention belongs to the technical field of intelligent traffic, and discloses a driver workload prediction method based on multiple physiological signals under various hearing interferences, which is created by combining three music rhythms (no, slow and fast), two navigation modes (high frequency and low frequency) and three traffic scenes (conventional roads, school roads and construction site roads). Multi-modal data including EDA, ECG, EEG, driving behavior characteristics and subjective workload evaluation is collected, the data is used for constructing a workload prediction model, a stacking model is adopted, and the stacking model integrates a plurality of machine learning models to improve prediction performance.

The application provides a workload prediction method for cloud-native applications and related equipment. The method comprises: performing tree-shaped aggregation processing on operation and maintenance time series data based on the internal data weight of the obtained operation and maintenance time series data to obtain an operation and maintenance time series data aggregation tree; calculating the correlation coefficient of each vector in the operation and maintenance time series data aggregation tree, and obtaining a characteristic time series data vector corresponding to the operation and maintenance time series data aggregation tree based on the correlation coefficient; and performing prediction on the characteristic time series data vector by using an information extraction network with introduced residual connection to obtain a prediction result corresponding to the operation and maintenance time series data. In the embodiment of the application, residual connection is introduced in the main encoder, so that even if the network depth is stacked, the output layer can directly obtain more input information, thereby maintaining the stability of model training. The generated adversarial network is introduced, the gradient is updated based on the optimal transmission distance for each iteration of the value function network, and then the weight is cut, thereby effectively solving the problem of long sequence state gradient explosion.

The invention provides a workload prediction method and device based on job information perception and time difference, and relates to the technical field of computing resource management, and the method comprises the steps: obtaining historical workload data of target equipment in a preset time period and job information related to a workload; extracting a time difference feature of the historical workload data, and fusing the time difference feature with the job information to generate a job load fusion feature; and inputting the job load fusion feature into a pre-trained conditional denoising diffusion model to obtain a job load prediction result of a future time period output by the conditional denoising diffusion model. According to the work load prediction method and device based on work information perception and time difference provided by the invention, the work information and the multi-order time difference feature are fused into the guide condition, and the generation capability of the condition de-noising diffusion model is utilized, so that high-robustness and high-precision probability prediction of work load mutation in a complex computing environment is realized.

Embodiments of this specification provide a workload prediction method and apparatus for a service in a service cluster. The method includes: obtaining a load indicator sequence of each service in a service cluster corresponding to a workload indicator in a same historical time period; determining, based on the load indicator sequence corresponding to each service, a service representation corresponding to each service; performing clustering processing based on the service representation corresponding to each service, to obtain a target category cluster to which each service belongs in multiple category clusters; obtaining multiple sequence prediction models pre-trained for multiple tasks, and enabling the multiple category clusters to correspond to the multiple tasks; and inputting at least a load indicator sequence of any service into a target sequence prediction model corresponding to a target category cluster of the service in the multiple sequence prediction models.

The embodiment of the invention provides a workload prediction method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining all variables of a target software engineering project; performing abnormal variable processing and missing variable processing on the variables to obtain processed variables; the processed variables are checked, and when a check value meets a set standard, a lithotripsy map is established; processing the lithotripsy map by using an intelligent optimization algorithm to obtain the number of common factors; determining a load coefficient matrix according to the number of the processed variables and common factors; performing coordinate transformation on the processed variable to obtain a value of a common factor; normalizing the value of the common factor to obtain a standard factor of the target software engineering project; and inputting the standard factor into a pre-trained random forest model, and predicting the workload of the target software engineering project, so that the prediction accuracy of the workload in the software engineering can be improved.

Methods and systems for managing operation of a deployment comprising data processing systems are disclosed. The operation may be managed by optimizing data processing systems task allocation based on at least future workload predictions of the data processing systems. The at least future workload predictions may be generated by a trained inference model (e.g., a convolution neural network, a temporal graph network, etc.). A data processing system may be selected to perform the task by using the at least future workload predictions to minimize an objective function. The data processing system having a future workload prediction of the at least future workload predictions that minimizes the objective function may be selected. Once the data processing system has been selected, the task may be performed by the data processing system.

According to some embodiments, methods and systems can be associated with a cloud computing environment. A workload prediction framework can receive observed workload information associated with a database (e.g., a database as a service (“DBaaS”)) in the cloud computing environment. Based on the observed workload information, a statement arrival rate (“SAR”) prediction can be generated. Further, a host variable assignment prediction can be generated based on the observed workload information. The workload prediction framework can then automatically create a workload prediction for the database using the SAR prediction and the host variable assignment prediction. A physical database design advisor (e.g., a table partition advisor) can receive the workload prediction and, in response to the workload prediction, automatically generate a recommended physical layout for the database (e.g., using a cost model, a current physical layout, and an objective function).

The application belongs to the technical field of intelligent transportation, and discloses a driver workload evaluation method based on galvanic skin signal under multiple auditory interferences, different driving scenes are investigated to study the comprehensive influence of navigation frequency and music rhythm on driver workload, a double-index method is adopted to combine subjective evaluation and galvanic skin signal, the SCL distribution of the galvanic skin signal and the subjective evaluation score show strong and consistent correlation, which provides a solid foundation for the galvanic skin signal as the main physiological index of the workload prediction model, and makes the driver workload prediction based on the galvanic skin signal under multiple auditory interferences more accurate.