Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach

TL;DR

This paper introduces a distributed ADMM-based method for scalable VNF load balancing and scaling in large-scale datacenters, addressing traffic fluctuations efficiently with minimized costs.

Contribution

It develops a novel distributed optimization framework using ADMM for joint load balancing and VNF scaling in large datacenters, improving scalability over centralized methods.

Findings

Effective in balancing traffic loads in large datacenters.

Reduces deployment and forwarding costs significantly.

Scalable and efficient for large-scale network functions management.

Abstract

Network Functions Virtualization (NFV) is a promising network architecture where network functions are virtualized and decoupled from proprietary hardware. In modern datacenters, user network traffic requires a set of Virtual Network Functions (VNFs) as a service chain to process traffic demands. Traffic fluctuations in Large-scale DataCenters (LDCs) could result in overload and underload phenomena in service chains. In this paper, we propose a distributed approach based on Alternating Direction Method of Multipliers (ADMM) to jointly load balance the traffic and horizontally scale up and down VNFs in LDCs with minimum deployment and forwarding costs. Initially we formulate the targeted optimization problem as a Mixed Integer Linear Programming (MILP) model, which is NP-complete. Secondly, we relax it into two Linear Programming (LP) models to cope with over and underloaded service chains. In the case of small or medium size datacenters, LP models could be run in a central fashion with a low time complexity. However, in LDCs, increasing the number of LP variables results in additional time consumption in the central algorithm. To mitigate this, our study proposes a distributed approach based on ADMM. The effectiveness of the proposed mechanism is validated in different scenarios.