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Service load fine-grained prediction method based on AKX hybrid model

A hybrid model and prediction method technology, applied in the field of service computing, can solve problems such as single model, ignoring a large amount of historical data for effective analysis and utilization, lack of effective real-time correction of prediction results, etc., to achieve the effect of ensuring availability

Pending Publication Date: 2020-11-10
HARBIN ENG UNIV
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Problems solved by technology

[0006] The present invention solves the problem that the existing service load prediction method has a single model, or lacks effective real-time correction of the prediction results, or ignores the effective analysis and utilization of a large amount of historical data, etc., and cannot well meet the requirements of the command system under certain backgrounds. Accuracy and low latency requirements and other issues

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  • Service load fine-grained prediction method based on AKX hybrid model
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  • Service load fine-grained prediction method based on AKX hybrid model

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specific Embodiment 1

[0070] The present invention provides a service load fine-grained prediction method based on the AKX hybrid model, specifically:

[0071] A method for fine-grained forecasting of service load based on the AKX hybrid model, comprising the following steps:

[0072] step 1:

[0073] Collect online real-time data generated during service operation monitoring, and perform data preprocessing on the collected service load data to obtain online real-time service load data sets;

[0074] The step 1 is specifically:

[0075] Collect service load data during the service operation monitoring process, including CPU utilization and memory utilization, and perform data preprocessing on the collected CPU utilization and memory utilization, remove the noise in the data, and finally obtain the service load data set.

[0076] Step 2: Perform white noise detection on the collected service load data set to ensure the availability of the service load data set;

[0077] The step 2 is specificall...

specific Embodiment 2

[0126] From figure 1 It can be seen that with the method provided by the present invention, online real-time service load prediction and correction based on Kalman will be performed from multiple perspectives, as well as XGBoost offline prediction correction using historical service load data. First, the service load will be online through the time series ARIMA model. Real-time service load prediction, and then input the service load prediction value based on the ARIMA model into Kalman for forecast correction, considering the value of historical service load data and the data accuracy problem caused by the difference calculation of the unstationary sequence in the data preprocessing process, and ARIMA For the residual problem caused by the nonlinear part in the model, XGBoost autoregressive machine learning will be performed on the service load historical data offline to more comprehensively correct the online real-time service load prediction based on the ARIMA-Kalman model, ...

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Abstract

The invention relates to a service load fine-grained prediction method based on an AKX hybrid model. The method belongs to the technical field of service computing. The method comprises the steps of collecting and preprocessing data generated in the service operation monitoring process, and performing white noise and stability detection on a data set to construct an ARMA model; establishing an ARIMA model, carrying out online real-time correction on a model prediction value by adopting a Kalman filtering method, and effectively processing a nonlinear residual error; and introducing an XGBoostmethod to carry out offline autoregression prediction training analysis on the historical data of the service load, carrying out difference calculation on the historical data of the service load and actual service load data, and carrying out fitting on a difference result and a prediction value based on the hybrid model to obtain a final service load prediction result. Compared with an existing prediction method, the method has higher prediction precision and lower time delay under the background of resource limitation and high load, and better meets the requirements of a command and control system for service effectiveness, reliability and high resource utilization rate under the task burst background.

Description

technical field [0001] The invention relates to the technical field of service computing, and relates to a service load fine-grained prediction method based on an AKX hybrid model. Background technique [0002] At present, the international situation is ups and downs, complex and changeable, and the risks and challenges of military wars are still severe. The new type of warfare in the future has the characteristics of high speed, great maneuverability, fast pace, and coordinated operations. As the center of the entire battlefield, the command and control system controls the situation and success of the war. challenge. Microservice technology is the main system architecture and mainstream implementation method of the current command and control system. Microservice load prediction through the analysis of historical data is an important method to ensure the effectiveness, reliability and high resource utilization of services. How to successfully implement combat missions and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50
CPCG06F9/505G06F2209/5019
Inventor 王勇曲连威马宇良王昊
Owner HARBIN ENG UNIV
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