  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.
