B-Scale: Bottleneck-aware VNF Scaling and Flow Routing in Edge Clouds

With the ever-growing demand for low-latency network applications, edge computing emerges as a new paradigm that provides computation and storage resources in close proximity to end-users. Many research efforts have resorted to network function virtualization, wherein network applications are provisioned as service function chains at edge clouds. However, due to the traffic dynamics and limited resource capacity at the network edge, how to efficiently embed service chains with latency optimization and resource efficiency remains as a challenging problem. As most existing research efforts largely overlook the bottle-necked resources of VNFs in the VNF scaling, we seek a more realistic approach to provisioning VNF instances across multiple edge clouds. Also, given the limited resources at the edge, it is of significant importance to improve the VNF utilization rate. Specifically, we formulate the VNF scaling problem as an integer linear programming (ILP) problem, aiming to minimize the end-to-end latency for service function chains. To solve this problem, we devise a novel bottleneck-aware algorithm that manages the number and deployment of newly created instances. After that, we propose an online algorithm for traffic steering to improve the utilization rates of VNF instances and avoid congestion on hotspot links. The proposed algorithm is shown to provide good performance by trace-driven simulation in real-world topologies.