MG-WEP: Multi-Granularity Workload Ensemble and Variational Inference for Multivariate Computing Power Prediction

AlthoughComputing Power Network(CPN) as the new network computing paradigm which can fully improve the utilization rate of decentralized computing power resources, the dynamic and heterogeneous characteristics of multivariate workloads present significant challenges to maintaining theQuality of Service(QoS) under dynamic resource scheduling. Therefore, workload prediction should be considered to ensure elastic demand services. However, the existing workload prediction methods mainly focus on (1) a single-granularity perspective, and (2) struggle to adapt to dynamic and heterogeneous multivariate workload environments, such as traditional LSTM-based or CNN-based methods that fail to capture cross-granularity dependencies under varying workload patterns. To consider above problems, we proposeMulti-Granularity Workload Ensemble and Feature Inference for Multivariate Computing Power Prediction(MG-WEP), which address the problem from a multi-granularity perspective. First, we develop a mutual information feature selection method using a variational inference network to identify key features, facilitating a comprehensive exploration of the relationships among workload variables from an attribute perspective. Then, the clustering method is used to cluster similar workloads, effectively capturing the relationships among them. Furthermore, a combined ensemble prediction method is applied on all clustered workloads to improve prediction accuracy by leveraging the distinctive characteristics of each cluster from object perspective. Finally, we have fully compared the proposed algorithm with eleven comparison methods and four evaluation metrics on three real-world workload trace datasets. The results show that the proposed method has superior prediction performance.