# Research on a Service Load Prediction Method Based on VMD-GLRT
###### Abstract  
In this paper, a deep learning-based prediction model VMD-GLRT is proposed to address the accuracy problem of service load prediction. The VMD-GLRT model combines Variational Mode Decomposition (VMD) and GRU-LSTM. At the same time, the model incorporates residual networks and self-attentive mechanisms to improve accuracy of the model. The VMD part decomposes the original time series into several intrinsic mode functions (IMFs) and a residual part. The other part uses a GRU-LSTM structure with ResNets and Self-Attention to learn the features of the IMF and the residual part. The model-building process focuses on three main aspects: Firstly, a mathematical model is constructed based on the data characteristics of the service workload. At the same time, VMD is used to decompose the input time series into multiple components to improve the efficiency of the model in extracting features from the data. Secondly, a long and short-term memory (LSTM) network unit is incorporated into the residual network, allowing the network to correct the predictions more accurately and improve the performance of the model. Finally, a self-focus mechanism is incorporated into the model, allowing the model to better capture features over long distances. This improves the dependence of the output vector on these features. To validate the performance of the model, experiences were conducted using open-source datasets. The experimental results were compared with other deep learning and statistical models, and it was found that the model proposed in this paper achieved improvements in mean absolute percentage error (MAPE).  
deep learning; cloud computing; microservices; time series analysis +
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applications, but workflow scheduling is still a fundamental problem to be studied [2]. At the same time, the waste of resources caused by low service load cannot be ignored. These problems lead to the resources not being fully utilized, so there is a contradiction between the demand for software and hardware resources and the imbalance of utilization. Currently, the most popular cloud deployment management method is Kubernetes (K8s) cluster management, which allows for more precise resource configuration under the original processing method. As such, this paper is based on K8s as the foundation for service deployment. Finding the optimal load level for different services and resolving inefficient resource scheduling based on existing resource conditions to ensure efficient software operation is a complex problem.  
To address these issues and ensure that the software maintains an appropriate resource allocation method across different periods, resource allocation is typically carried out in advance in the same historical period, relying on experience-based judgment. However, as software continues to evolve and update, a manual review can become a cumbersome task when there are too many services, compromising the accuracy of the decision, particularly when expanding traditional applications, resulting in a higher cost. Therefore, the value of using an empirical judgment approach is invaluable and far outweighs the benefits, which do not meet the current needs of microservice software development and deployment. Furthermore, incorrect predictions can lead to Service Level Agreement (SLA) violations, resulting in a significant waste of resources or system instability [3]. Accurately predicting the performance of application services is essential for effective scheduling and resource allocation, and can be beneficial for service migration and efficient resource allocation [4].  
Exploring how to effectively leverage the fast response of edge servers and the powerful processing of global schedulers to address workflow scheduling problems has great potential for research [5]. Consequently, a prediction-based resource allocation is urgently needed to maximize the value of applications with limited resources [6].