Study on load prediction methods of embedded container-based applications

Currently, the use of virtual container architectures in embedded computing environments is becoming increasingly popular, offering new possibilities for resource scheduling to balance load fluctuations. However, effective scheduling relies heavily on accurate load prediction, and existing research in this area suffers from a lack of dedicated datasets and inadequate adaptation of existing prediction methods to the characteristics of embedded applications. Focusing on an avionics embedded application scenario, a dataset tailored to containerized embedded environments is constructed. To address the issues of low prediction accuracy and computational inefficiency, a lightweight load prediction model is proposed by integrating the CEEMDAN algorithm with the Informer model. The CEEMDAN algorithm enhances the modeling accuracy by decomposing the time series data, while the Informer model reduces the computational complexity and memory consumption through a sparse self-attention mechanism. Experimental results demonstrate that, comparing with the mainstream time series prediction methods, the present model achieves an average reduction of about 10% in prediction errors and is well-suited for embedded application scenarios.