Online VNF Scaling in Datacenters

TL;DR

This paper presents online algorithms for dynamic VNF provisioning in datacenter networks, optimizing resource allocation for fluctuating traffic without future traffic knowledge, with proven competitive ratios and simulation validation.

Contribution

It introduces novel online algorithms for VNF scaling in cloud datacenters, achieving competitive ratios and handling multiple service chains efficiently.

Findings

Efficient randomized algorithm for single service chain with e/(e-1) competitive ratio.

Heuristic algorithm for multiple chains with O(1) competitive ratio.

Validated effectiveness through theoretical analysis and trace-driven simulations.

Abstract

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.