Fast Long Sequence Time-Series Forecasting for Edge Service Running State Based on Data Drift and Non-Stationarity
Zhiqiang Zhang, Dandan Zhang, Yun Wang · IEEE Transactions on Knowledge and Data Engineering · 2024
The operational state of edge services in remote areas and complex geographical work environments is influenced by scarce hardware resources, unstable communication state and external dynamic environment. This leads to the continuous evolution of reliability data streams of edge services in the form of data drift, producing non-stationary data streams with substantial amounts of noise data, resulting in the difficulty of service operational status prediction and low efficiency. Previous studies mainly utilize stationarity-based methods to attenuate the non-stationarity of original sequence, aiming to enhance predictability using deep learning models. However, stationary series deprived of their inherent non-stationarity struggle to accurately forecast practical emergencies. Furthermore, the size and computational complexity of deep learning models are unsuitable for deployment at the edge. To accurately predict the reliability of edge services in dynamic environments, we propose a lightweight and fast long sequence time-series forecasting method based on data drift and non-stationarity, named FSNet. FSNet introduces a non-stationary information sampling factor to extract external factors influencing data flows and incorporates Moore-Penrose inverse matrix to swiftly update the model weights during runtime. Combining unmanned aerial vehicles as mobile edge servers with edge computing offloading achieves collaborative computation of the model, thereby alleviating the scarcity of resources at the edge, enhancing computational efficiency, and enabling fast and reliable prediction of service operations. Extensive experimental results validate the effectiveness and efficiency of the FSNet.