Cloud computing load prediction method based on deep belief network

Abstract

The invention provides a cloud computing load prediction method based on a deep belief network. The cloud computing load prediction method comprises the following steps of (1) extracting and aggregating load observed values from a cloud group; (2) performing differential transformation on the observed values of the step (1), so as to reduce the linearity of data, normalizing the data, and analyzing autocorrelation and autoregression characteristics in the data; (3) training RBM (Restricted Boltzmann Machines) layer by layer from bottom to top, and optimizing a whole network structure through a BP (Back Propagation) algorithm; (4) performing short-term and long-term prediction on cloud attributes by using the network trained in the previous step; (5) performing inverse transformation opposite to the step (2) on prediction results in the step (4), so as to obtain predicted values of raw data. The cloud computing load prediction method has the advantages of small prediction error, being suitable for long-term prediction and the like, a reliable basis can be provided for resource scheduling of a cloud platform, and the effects of efficient scheduling and use of cloud resources are achieved.

Description

technical field [0001] The invention relates to the field of cloud computing big data computing and computing intelligence, in particular to a cloud computing load prediction method based on a deep belief network. Background technique [0002] It is extremely difficult to predict the load in a cloud computing environment. Compared with grid computing and high-performance computing, due to the highly variable time and space of user interaction with the cloud platform and the type of cloud tasks uploaded, the cloud load exhibits a highly nonlinear nature, resulting in the traditional linear Or the probabilistic model does not show its good effect when dealing with grid and high-performance systems. [0003] As a member of the deep neural network, the deep belief network is a multi-layer neural network composed of RBM (Restricted Boltzmann Machines). An RBM can obtain the internal mode or characteristics of the data, and a deep belief network composed of multiple RBMs can obt...

AI Technical Summary

Problems solved by technology

In this way, a good prediction result requires cloud attributes to have self-similarity, which also limits its application range

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