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.

翻译：本文通过基于角蜥优化算法优化卷积神经网络-双向门控循环单元的数据回归算法，开展了云计算能耗的预测研究。首先，通过对CPU使用率、内存使用率、网络流量、功耗、执行指令数、执行时间和能效进行Spearman相关性分析，发现功耗与能效的正相关性最高，而CPU使用率与能效的负相关性最高。实验中，我们引入随机森林模型和基于角蜥优化算法的优化模型进行测试，结果表明优化算法相比随机森林模型具有更好的预测效果。具体而言，优化算法的均方误差（MSE）比随机森林模型小0.01，平均绝对误差（MAE）比随机森林模型小0.01。综合指标结果显示，优化算法在预测能效方面表现更为准确可靠。该研究成果为提高云计算系统的能效提供了新的思路和方法。本研究不仅拓展了在云计算领域的应用范围，也为提升系统能源利用效率提供了有力支撑。