OCPNet: A deep learning model for online cloud load prediction

Abstract

Accurate prediction of cloud platform load contributes to the optimal allocation of cloud platform resources, and is an important means to solve resource scheduling problems and effectively manage cloud resources. However, most previous studies on cloud load prediction are based on offline settings, lacking scalability in realistic scenarios where data streams constantly arrive. Online real-time prediction of cloud loads can improve prediction efficiency, realizing fast response and dynamic adjustment to sudden loads, effectively minimizing resource wastage and enhancing system robustness. Therefore, we propose a deep learning—based online cloud load prediction network, OCPNet. It employs a forward architecture of learning module stacking, which progressively expands the receptive field of the convolutional kernel inside the learning module by exponentially growing the dilation factor to acquire short- and long-term features. Additionally, an online learning mechanism incorporating memory capabilities is proposed, which utilizes a fast learner to complete the learning of data streams, and a Pearson trigger to initiate the dynamic interaction between the memorizer and fast learner, thereby reducing the concept drift’s impact. Moreover, we propose a feature extractor that enriches the data features of variables by accomplishing the extraction of variable relationships using the flip and multi-attention mechanisms. In experiments on Huawei Cloud and Microsoft Cloud workload datasets, OCPNet is compared with current mainstream deep learning models for cloud workload prediction. Results indicate that OCPNet’s online multivariate and univariate prediction mean square error decreases by 25.5% and 35.5%, respectively, compared with the best deep learning baseline models.