Efficient Online Scheduling of Service Function Chains Across Multiple Geo-Distributed Regions

Traditional network functions are typically implemented using specialized hardware appliances, which are expensive and difficult to upgrade. Network Function Virtualization (NFV) offers an effective approach to address these challenges by implementing comparable functionalities on commercial servers through software-based virtualization. In NFV, a sequence of Virtual Network Functions (VNFs) is orchestrated to form a Service Function Chain (SFC) that provides flexible network services. However, scheduling SFCs with multiple resource constraints to achieve high reliability poses a critical challenge. Existing approaches often assume offline scheduling and overlook the dynamic nature of heterogeneous resource loads across regions. Moreover, they primarily focus on individual VNFs rather than considering the cross-region scheduling of the entire SFC, which can result in increased transmission delay. In this paper, we investigate the problem of service function chain scheduling across multiple regions (SFCS-MR) with deadline constraints, aiming to maximize the success rate of requests. We formulate this problem as an Integer Linear Programming (ILP) model and prove its NP-hardness. To address this problem effectively, we propose a two-stage algorithm that determines whether an SFC requires cross-region scheduling and selects the suitable regions for its execution. Through extensive experimental evaluations, we demonstrate that our cross-region SFC scheduling solution can achieve a maximum improvement of 32.42% in the overall request success rate compared to benchmarks.