Method for predicting elastic cloud computing resources based on SARIMA-WNN model

Abstract

The invention discloses a method for predicting elastic cloud computing resources based on an SARIMA-WNN model. The method comprises the steps that complementary advantages are achieved by using a seasonal autoregressive integrated moving average (SARIMA) model combining with a wavelet neural network (WNN) prediction model to improve the prediction accuracy; according to the SARIMA, seasonal periodic factors are added on the basis of an ARIMA model, periodic cloud resource demand data of a past section is input to a SARIMA (q, d, q)(P, D, Q) s model to obtain d, p, q, D, P and Q respectively;prediction is conducted on tranquilized and sequenced codes by using the SARIMA model, and a prediction result is marked as and an L residual value is marked as rt, wherein the prediction result andthe residual value can be obtained through the prediction; a model which conforms to elastic cloud source prediction is obtained through conducting training on the WNN network by using training samples, prediction is conducted aiming at the residual sequence rt, and the prediction result is marked as ; finally the prediction result of the SARIMA-WNN combined model is obtained. By means of the method, the problems of inaccuracy of a single model, poor effects of other combined models and the like are solved.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and in particular relates to a method for predicting elastic cloud computing resources based on a SARIMA-WNN model. Background technique [0002] In view of the current popularization and wide application of cloud computing, the cloud computing center provides tenants with resources in the form of virtual machines (VMs). Sufficient resources are prepared, but service failures for tenants caused by resource allocation lags, frequent SLA breaches, and excessive resource supply lead to a large amount of waste of cloud computing resources. In order to achieve ideal supply and demand balance, so efficient algorithms related to elastic cloud resources emerge as the times require. [0003] Elastic algorithms widely used in cloud computing include Autoregressive Integral Moving Average Model (ARIMA), ARIMA-improve, Back Propagation Neural Network (BPNN), SARIMA, etc. Elastic cloud resource dema...

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Problems solved by technology

[0002] In view of the current popularization and wide application of cloud computing, the cloud computing center provides tenants with resources in the form of virtual machines (VMs). Sufficient resources are prepared, but service failures for tenants caused by resource allocation lags, frequent SLA breaches, and excessive resource supply lead to a large amount of waste of cloud computing resources. In order to achieve ideal supply and demand balance, so an efficient elastic cloud resource-related algorithm emerges as the times require

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