  Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital
for efficient application deployment and resource provisioning. However, the
heterogeneous application patterns, variable infrastructure performance, and
frequent deployments in MT-ECP pose significant challenges for accurate and
efficient workload prediction. Clustering-based methods for dynamic MT-ECP
modeling often incur excessive costs due to the need to maintain numerous data
clusters and models, which leads to excessive costs. Existing end-to-end time
series prediction methods are challenging to provide consistent prediction
performance in dynamic MT-ECP. In this paper, we propose an end-to-end
framework with global pooling and static content awareness, DynEformer, to
provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously
designed global pooling and information merging mechanisms can effectively
identify and utilize global application patterns to drive local workload
predictions. The integration of static content-aware mechanisms enhances model
robustness in real-world scenarios. Through experiments on five real-world
datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP
and provided a unified end-to-end prediction scheme for MT-ECP.
