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arXiv · 2407.14575

Regression prediction algorithm for energy consumption regression in cloud computing based on horned lizard algorithm optimised convolutional neural network-bidirectional gated recurrent unit

Feiyang Li·Zinan Cao·Qixuan Yu·Xirui Tang
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Abstract

For this paper, a prediction study of cloud computing energy consumption was conducted by optimising the data regression algorithm based on the horned lizard optimisation algorithm for Convolutional Neural Networks-Bi-Directional Gated Recurrent Units. Firstly, through Spearman correlation analysis of CPU, usage, memory usage, network traffic, power consumption, number of instructions executed, execution time and energy efficiency, we found that power consumption has the highest degree of positive correlation with energy efficiency, while CPU usage has the highest degree of negative correlation with energy efficiency. In our experiments, we introduced a random forest model and an optimisation model based on the horned lizard optimisation algorithm for testing, and the results show that the optimisation algorithm has better prediction results compared to the random forest model. Specifically, the mean square error (MSE) of the optimisation algorithm is 0.01 smaller than that of the random forest model, and the mean absolute error (MAE) is 0.01 smaller than that of the random forest.3 The results of the combined metrics show that the optimisation algorithm performs more accurately and reliably in predicting energy efficiency. This research result provides new ideas and methods to improve the energy efficiency of cloud computing systems. This research not only expands the scope of application in the field of cloud computing, but also provides a strong support for improving the energy use efficiency of the system.

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Feiyang Li, Zinan Cao, Qixuan Yu, Xirui Tang. 2024-07-19. Regression prediction algorithm for energy consumption regression in cloud computing based on horned lizard algorithm optimised convolutional neural network-bidirectional gated recurrent unit. https://arxiv.org/abs/2407.14575

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