Abstract
Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined prediction energy model of adaboost algorithm and RBF (radial basis function) neural network at software architecture level is proposed. Firstly, three kinds of energy prediction models are established by polynomial regression, support vector machine and neural network respectively. Secondly, the RBF neural network is used to nonlinear combine the predicted values of the above three models. Finally, RBF integrated by adaboost algorithm is used as high-precision prediction of energy consumption. Experimental results show that the prediction accuracy of the combined prediction model is higher than that of the single model.
Highlights
ICT industry is an important driving force for social development and world economic growth in the 21st century
Its energy consumption accounts for 10% of the total global power consumption [1]; its total carbon emissions reach 2% ~ 2.5% of the total global carbon emissions, which is more obvious in developed countries, reaching 10% [2]
According to a research report from the European Union, if the temperature rise is to be controlled below 2 °C in 2020, the carbon emission must be reduced by 15%-30% [3]
Summary
ICT (information and communication technology) industry is an important driving force for social development and world economic growth in the 21st century. RELATED WORK theories of power model, this paper studies the accuracy of Researchers and scientists have proposed many software power model of complex network representation at software power models to achieve the energy Colmant [22] has focused on the energy estimation of VM-based systems and has proposed a fine-grained monitoring middleware called BitWatts It provided real-time and power estimation of software processes running at any level of virtualization in the system, which can automatically learn an application-agnostic power model to estimate the power consumption of applications. Based on [27], this paper improves the energy consumption prediction effect at the architecture level through the combination prediction model of Adaboost RBF method. When the error is returned in the reverse direction, the network adjusts the network weight and threshold according to the deviation between the output value and the expected output value, so that the output tends to the expected value in the near future
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