34 results about "Workload prediction" patented technology

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 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 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 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.

A model-as-a-service (MaaS) platform includes a multimodal model instance that is executed by dedicated groups of processing resources allocated to support different modality-specific processing pipelines. The MaaS platform further includes an intelligence layer that tracks modality-specific token utilization over time; generates a modality-specific workload prediction for the multimodal model instance based on the tracked data; and generates a modality-specific latency prediction based on the modality-specific workload prediction. The MaaS platform further includes a resource allocation component that uses the modality-specific latency prediction to dynamically reallocate the processing resources among the dedicated groups supporting the different modality-specific processing pipelines.

The invention relates to the technical field of data center heat dissipation, and provides a data center heat dissipation method and device based on workload prediction and electronic equipment, and the method comprises the steps: predicting the node-level workload distribution of a data center in a future time period according to historical and real-time workload data through employing a load prediction model; utilizing the cooling capacity prediction model to predict a cooling capacity demand in a future time period, and determining a business migration strategy and an optimal operation parameter; the service migration strategy is used for optimizing load distribution and cold energy supply efficiency of the data center by adjusting service distribution; the optimal operation parameters are used for achieving dynamic matching of the cooling capacity and the required cooling capacity of each refrigeration unit; and executing the service migration strategy, and adjusting the operation of the data center refrigeration system according to the optimal operation parameters. Accurate load prediction can be achieved, service distribution adjustment is matched, the refrigeration efficiency can be improved, energy consumption is reduced, the reliability and stability of the data center are improved, and normal operation of the data center is guaranteed.

A method and system for adaptive hybrid data partitioning of a database based on reinforcement learning, which belongs to the field of artificial intelligence. The present invention solves the problem that under the existing data partitioning scheme, a large amount of redundant data needs to be accessed when executing the query load and the data cannot be automatically adaptively partitioned according to the dynamic workload. The present invention establishes a workload prediction model, and predicts the frequency of various types of queries in the query workload of the database in the future based on the historical workload. The hybrid data partitioning problem is modeled as a Markov decision process, and the state, action and reward of the intelligent agent are defined. According to the predicted frequency of various types of queries and the trained reinforcement learning model, a suitable hybrid data partitioning scheme is adaptively recommended for the incoming query workload, thereby reducing the amount of data that needs to be accessed when the query load is executed. The method of the present invention can be applied to adaptive hybrid data partitioning of databases.

The invention provides an end-to-end service level adaptive cloud data center resource collaborative optimization method. The method is characterized by comprising the following steps of: predicting a workload based on historical task data; under the constraint of global SLA violation budget, dynamic violation budget allocation and resource quota calculation are carried out on each application type based on dynamic planning; calculating the effective capacity of the virtual machine based on a historical utilization rate and a security margin factor, and supplying a security excess allocation virtual machine; a virtual machine-to-physical machine placement step based on an anti-affinity principle; and a double-layer load balancing task scheduling step based on deep reinforcement learning. According to the method, workload prediction, SLA violation budget allocation, resource supply and placement and online task scheduling are unified in one framework, and the problem of global sub-optimization caused by isolated optimization of a resource configuration layer and a scheduling layer is avoided.

The invention relates to the technical field of database resource management, in particular to a database workload prediction method and device based on a core set, and the method comprises the steps: disassembling SQL statements in the core set into operation, table / column, JOIN condition, predicate and clause quintuples to generate multi-dimensional feature vectors; a preset Map-Reduce framework is utilized to carry out disassembling and dynamic blocking on the multi-dimensional feature vector, and multi-weight feature clustering is carried out on each time sequence block to obtain clustering features; and inputting the clustering features into the target mixed feature prediction model to predict the workload of the database. Therefore, the problems that a traditional workload prediction method is high in training cost, low in prediction precision, poor in expandability and incapable of adapting to dynamic loads in the prior art are solved.