Scout: Tailored Collaborative Workload Forecasting for Multi-Tenant Edge Cloud Platforms

Efficient workload forecasting is pivotal for both service orchestration and request dispatching in quality of service (QoS)-oriented multi-tenant edge cloud platforms (MT-ECPs) with a native tiered architecture. However, the spatial-temporal heterogeneity and structural constraints of native tiered architecture present significant challenges for the forecasting in sophisticated MT-ECPs. To tackle these challenges, we propose SCOUT, which is a novel Self-supervised learning-enhanced Cloud-edge collabOrative Unified workload forecasTing framework. First, we design a cross-granularity collaborative mechanism that enables SCOUT to balance accuracy and efficiency in forecasting within the tiered architecture of MT-ECPs. Notably, we employ an auxiliary self-supervised learning method at the cloud that enhances workload pattern representations, making them reflective of both spatial and temporal heterogeneity. Extensive experiments on two real-world workload datasets show that SCOUT outperforms state-of-the-art methods for MT-ECP's workload forecasting, decreases time consumption and reduces communication costs.