Approximation Algorithms for the NFV Service Distribution Problem

TL;DR

This paper develops fast approximation algorithms for the NFV service distribution problem, optimizing VNF placement, routing, and resource allocation in distributed cloud networks to minimize costs.

Contribution

It introduces the QNSD algorithm for load-proportional costs and a variation for integer resource costs, advancing efficient NFV service distribution solutions.

Findings

QNSD achieves an O(ε) approximation with O(1/ε) time.

The algorithms effectively minimize cloud and network costs.

The methods handle both fractional and integer resource cost scenarios.

Abstract

Distributed cloud networking builds on network functions virtualization (NFV) and software defined networking (SDN) to enable the deployment of network services in the form of elastic virtual network functions (VNFs) instantiated over general purpose servers at distributed cloud locations. We address the design of fast approximation algorithms for the NFV service distribution problem (NSDP), whose goal is to determine the placement of VNFs, the routing of service flows, and the associated allocation of cloud and network resources that satisfy client demands with minimum cost. We show that in the case of load-proportional costs, the resulting fractional NSDP can be formulated as a multi-commodity-chain flow problem on a cloud augmented graph, and design a queue-length based algorithm, named QNSD, that provides an O(\epsilon) approximation in time O(1/\epsilon). We then address the case in which resource costs are a function of the integer number of allocated resources and design a variation of QNSD that effectively pushes for flow consolidation into a limited number of active resources to minimize overall cloud network cost.