Abstract: Load prediction methods in edge computing environments are crucial for the allocation and management of computing resources.Edge load data has characteristics such as volatility,noise,mutation,and time dependence.Therefore,a single prediction model is difficult to effectively extract the multi-dimensional information of load data.To address the above problems,an edge load prediction method based on thehybrid s-TimeXer model is proposed.Firstly,the FFT-SSD collaborative decomposition module is constructed.The main period of the load data is extracted by Fast Fourier Transform as the window length parameter of singular spectrum decomposition,which enhances the ability to capture periodic oscillation structure and realizes the effective separation of trend term,period term and noise term.Then,the load data is embedded as an endogenous variable,and the characteristic subsequences of singular spectrum decomposition are embedded as exogenous variables to construct a multi-dimensional feature interaction space.The time dependency of the load data is captured through the self-attention mechanism,and the dynamic interaction between the load data and the characteristic subsequences of singular spectrum decomposition is achieved through the cross-attention mechanism,thereby enhancing the contribution of the periodic component and the trend component to the prediction target.At the same time,the Hyperband Pruner algorithm is introduced to achieve efficient optimization of hyperparameters and improve prediction accuracy.Through the decomposition-embedding joint optimization architecture,while inheriting the advantages of TimeXer time series modeling,the synergistic enhancement of noise suppression and multi-dimensional information extraction is achieved.Experimental results on the ECW and Alibaba datasets demonstrate that the s-TimeXer model surpasses multiple state-of-the-art baseline methods in prediction accuracy.Specifically,on the ECW dataset,the reductions are 27.7%~63.4% for MSE and 14.3%~46.5% for MAE;on the Alibaba dataset,the reductions are 39.7%~42.5% for MSE and 18.4%~23.8% for MAE.The s-TimeXer model can effectively improve the accuracy of edge load prediction and provide strong support for resource scheduling in edge computing environments.

Key words: Edge computing, Load prediction, Singular spectrum decomposition, TimeXer, Hyperband Pruner algorithm