SFC-HO: Reliable Layered Service Function Chaining

Abstract

Network function virtualization (NFV) utilizes IT virtualization technology to realize the functions of various network devices and realize the decoupling of hardware and software functions. Typically, a service request specifies the virtual network functions (VNFs) it needs and the order between them. A network flow needs to traverse a set of sequential VNFs called a service function chain (SFC). Although SFC can increase the flexibility of service orchestration to meet the needs of different users, network providers face many challenges due to the need to ensure service reliability and some constraints. Therefore, how orchestrating SFC and designing a suitable network architecture is a critical issue in this field at present. We propose an efficient network layer-based SFC orchestration method, called SFC-hierarchical orchestration (SFC-HO). Our method separates virtual and physical networks into layers and computes the availability of resources in each layer. We filter the VNFs of each layer and deploy them to the physical layer that meets the greatest benefit. To this end, we formulate the problem as an integer linear programming (ILP) problem to minimize the total deployment cost. In order to further optimize the layering strategy, we innovatively use the Benders decomposition method to decompose the SFC-HO problem into two subproblems, which are the hierarchical mapping problem of VNFs and the routing problem between nodes. We propose two algorithms for the two problems respectively. The simulation results show that compared with the SFC orchestration process in the traditional network model. Our research can effectively improve the reliability of the SFC, reduce the delay of the routing process, and effectively reduce the cost consumption. Our research effectively solves the problem of difficult service orchestration for operators in the face of diverse service demands and a large number of service requests.

Robust Virtual Network Function Optimization Under Post-Failure Uncertainty: Classical & Quantum Computi

“…Stochastic Integer Programming (SIP), like many other algorithms dealing with uncertainty, requires probability distribution of uncertain events that often may be impractical to achieve [11]. Other traditionally used robust optimization techniques, such as benders decomposition, and chance- [13]. Nonetheless, most of the traditional mathematical programming frameworks, such as SIP, ILP, Mixed Integer Linear Programming, and Constraint Programming models, are often prone to face the curse of dimensionality [11], resulting in high running-time complexity and incompatibility for modern real-time SFC applications.…”