Transient Resource Provisioning for Connected Autonomous Vehicles-Oriented Edge Slicing: A Learning-Based Two-Timescale Approach

Edge slicing is envisioned to support connected autonomous vehicle (CAV) applications with diverse key performance indicator (KPI) requirements by splitting the shared physical infrastructure into several virtual networks. Unfortunately, existing provisioning approaches struggle to accommodate the spatiotemporal dynamics of CAV traffic, leading to significant violated KPIs or soared resource usage. In this paper, we introduce the transient sharing mechanism among edge slices to obtain reused gains without generating harmful performance interference, in which a slice is allowed to access to the under-utilized reserved resources of other slices but may experience interruptions at any time. Considering the heterogeneity and uncertainty of transient resources, we further develop a two-timescale provisioning scheme. Specifically, slices proactively make reservation decisions based on multi-armed bandit architectures at the beginning of large timescales, while hinging on cost-incentive auction mechanisms selectively preempt transient resources in terms of real-time application demands at each small timescale. With extensive experiments based on real traffic traces, we demonstrate that the proposed scheme can improve 10.43% resource utilization and make slices reduce 42.92% cost than state-of-the-art works, which verifies its high assurance and adaptability.