A Collaborative Cloud-Edge Approach for Robust Edge Workload Forecasting

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge-limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose XELASTIC, which offers three key improvements over the conference version. First, we redesigned the aggregation and disaggregation layers using GCNs to capture more complex relationships among workload series. Second, we introduced a supervised contrastive loss to enhance robustness against outliers, particularly for handling missing or abnormal data in real-world scenarios. Finally, we expanded the evaluation with additional baselines and larger datasets. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider (Alibaba ENS) show that XELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication costs.