MSM-TFL: A Multiservice, Multitask Transformer Framework for Edge Load Prediction

Accurate load prediction ensures edge cluster stability, which is crucial for resource allocation and task scheduling. However, varied service levels in multi-service clusters cause uneven resource distribution, overloading some devices. To address this issue, this paper introduces a multiservice, multitask transformer framework for edge load prediction, referred to as MSM-TFL. This framework applies dual encoders to capture variations in service features over time and leverages the Continuous Time Screening (CTS) method to calculate adjacent time points or identify the most similar time points as inputs to the dual encoders using the Similarity Search Using Service Features (SSUSF) method. In tests conducted on 30 server clusters with five loads, MSM-TFL-Continue outperforms the SOTA approach in terms of prediction errors for 1-, 5-, and 15-minute loads, total CPU time, and user CPU time. Compared with iTransformer, MSM-TFL-Continue reduces the prediction error in terms of the MAE, RSE, RMSE, and MAPE by 77.29%, 70.06%, 73.90%, and 77.47%, respectively, when a 5-minute load is used as an example. The prediction errors in terms of the MAE, RSE, RMSE, and MAPE are decreased by 74.46%, 64.75%, 69.31%, and 72.45%, respectively, by applying MSM-TFL-Similarity.