Routing-Led Evolutionary Algorithm for Large-Scale Multi-Objective VNF Placement Problems

TL;DR

This paper introduces a novel parallel metaheuristic and efficient data structures for optimal virtual network function placement in large-scale data centers, achieving high-quality solutions for networks with up to 64,000 servers.

Contribution

It presents a new parallel evolutionary algorithm with fast heuristics and memory-efficient data structures tailored for large-scale VNF placement problems.

Findings

High-quality solutions for networks with 64,000 servers

Effective heuristics outperform existing methods on large instances

Memory-efficient data structures enable scalable computation

Abstract

Modern data centers contain thousands of servers making them major consumers of electricity. To minimize their environmental impact, it is critical that we use their resources efficiently. In this paper we study how to discover the optimal placement of virtual network functions in large scale data centers. We propose a novel parallel metaheuristic, fast heuristic objective functions of the QoS and new memory efficient data structures for large networks. We further identify a simple, fast heuristic that can produce competitive solutions to very large problem instances. Using these new concepts, we are able to find high quality solutions for data centres with up to 64,000 servers.