Host load forecasting method in cloud computing environment

Abstract

The invention provides a load forecasting method for a cloud computing center host, belongs to the cloud computing field and solves the problem that as virtual machines of different users work on a host of a cloud computing center and the host load is subject to more complicated changes, the host load has to be accurately forecasted to further dispatch the virtual machines so as to achieve the purposes of load balancing and energy consumption reduction. The core of the algorithm lies in that the phase-space reconstruction method in the chaos theory and the data grouping treatment algorithm based on genetic algorithm are combined together. Compared with the present existing method, by adopting the method provided by the invention, a smaller relative error can be obtained. Besides, under the condition that the forecasting time is prolonged, compared with the traditional method, the accumulated error of the forecasting is reduced.

Description

technical field [0001] The invention mainly aims at the problems of host scheduling and load balancing in the cloud computing environment, and proposes a method for predicting host load. Background technique [0002] Cloud computing is a computing model that provides dynamically scalable virtualized resources as a service through the Internet. For cloud service providers, various underlying resources (computing, storage, network, etc.) Transparent, users do not need to understand the internal structure of the cloud computing center, only care about whether their needs are met. [0003] For cloud service providers, centralized management of resources can effectively improve resource utilization. However, due to the heterogeneity of various underlying resources in the cloud computing center and the differences in user applications, the reasonable scheduling and load of resources Equilibrium becomes a challenging problem. An effective way to improve resource utilization is t...

AI Technical Summary

Problems solved by technology

The disadvantage of these methods is that the change of the host load is generally nonlinear, and it is difficult to predict the load of the host with a linear method. When the load of the host changes sharply, there will be a large deviation between the predicted value and the actual value.

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