Application-Driven Provisioning of Service Function Chains Over Heterogeneous NFV Platforms

Although network function virtualization (NFV) has been proven to be beneficial in terms of equipment cost, service delivery flexibility, and time-to-market, most of the studies in this area only addressed homogeneous NFV platforms (e.g., with virtual machines (VMs) only). In this work, we argue that by leveraging heterogeneous NFV platforms such as VMs, docker containers, and programmable hardware accelerators (e.g., SmartNICs), one could achieve better flexibility and cost-effectiveness to support virtual network function service chains (vNF-SCs) with various quality-of-service (QoS) requirements. Therefore, we study application-driven provisioning of vNF-SCs over heterogeneous NFV platforms, and design a polynomial-time approximation algorithm to tackle the problem for near-optimal solutions. We first introduce a layered auxiliary graph (LAG) based approach to model the problem of vNF-SC provisioning, and then formulate a novel integer linear programming (ILP) model based on it. Specifically, the ILP model minimizes the total cost of vNF-SC deployment while ensuring that the QoS requirements of all the vNF-SCs are satisfied. To solve the ILP time-efficiently, we propose an approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations confirm that with significantly improved time-efficiency, our proposed algorithm can provide near-optimal solutions whose gaps to the exact ones are bounded.