GROUP: An End-to-end Multi-step-ahead Workload Prediction Approach Focusing on Workload Group Behavior

Accurately forecasting workloads can enable web service providers to achieve proactive runtime management for applications and ensure service quality and cost efficiency. For cloud-native applications, multiple containers collaborate to handle user requests, making each container’s workload changes influenced by workload group behavior. However, existing approaches mainly analyze the individual changes of each container and do not explicitly model the workload group evolution of containers, resulting in sub-optimal results. Therefore, we propose a workload prediction method, GROUP, which implements the shifts of workload prediction focus from individual to group, workload group behavior representation from data similarity to data correlation, and workload group behavior evolution from implicit modeling to explicit modeling. First, we model the workload group behavior and its evolution from multiple perspectives. Second, we propose a container correlation calculation algorithm that considers static and dynamic container information to represent the workload group behavior. Third, we propose an end-to-end multi-step-ahead prediction method that explicitly portrays the complex relationship between the evolution of workload group behavior and the workload changes of each container. Lastly, enough experiments on public datasets show the advantages of GROUP, which provides an effective solution to achieve workload prediction for cloud-native applications.