One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
This addresses efficient resource provisioning for edge cloud operators, though it appears incremental as an improved end-to-end method for a specific domain bottleneck.
The paper tackles the challenge of accurate workload prediction in dynamic multi-tenant edge cloud platforms, where heterogeneous patterns and frequent deployments hinder existing methods. DynEformer, their proposed framework with global pooling and static content awareness, achieved state-of-the-art performance on five real-world datasets.
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.