Virtual Function Placement for Service Chaining with Partial Orders and Anti-Affinity Rules

TL;DR

This paper addresses the complex problem of optimally placing virtual network functions in service chains, considering partial order constraints, anti-affinity rules, and resource limitations, to minimize costs.

Contribution

It formulates the virtual function placement problem as an Integer Linear Program and proposes a heuristic algorithm that performs near-optimally for large-scale instances.

Findings

The ILP formulation effectively models the placement problem with multiple constraints.

The heuristic algorithm achieves near-optimal solutions in large-scale scenarios.

Cost-efficient virtual function deployment is feasible with the proposed approach.

Abstract

Software Defined Networking and Network Function Virtualization are two paradigms that offer flexible software-based network management. Service providers are instantiating Virtualized Network Functions - e.g., firewalls, DPIs, gateways - to highly facilitate the deployment and reconfiguration of network services with reduced time-to-value. They employ Service Function Chaining technologies to dynamically reconfigure network paths traversing physical and virtual network functions. Providing a cost-efficient virtual function deployment over the network for a set of service chains is a key technical challenge for service providers, and this problem has recently caught much attention from both Industry and Academia. In this paper, we propose a formulation of this problem as an Integer Linear Program that allows one to find the best feasible paths and virtual function placement for a set of services with respect to a total financial cost, while taking into account the (total or partial) order constraints for Service Function Chains of each service and other constraints such as end-to-end latency, anti-affinity rules between network functions on the same physical node and resource limitations in terms of network and processing capacities. Furthermore, we propose a heuristic algorithm based on a linear relaxation of the problem that performs close to optimum for large scale instances.