Demand-Aware Network Design with Steiner Nodes and a Connection to Virtual Network Embedding

TL;DR

This paper studies demand-aware network design with Steiner nodes, providing complexity insights, approximation algorithms, heuristics, and empirical validation on real datacenter traffic traces to optimize communication costs.

Contribution

It introduces a Steiner node variant of the bounded-degree network design problem, offers a constant-factor approximation algorithm, and empirically demonstrates improved network designs.

Findings

The Steiner node approach reduces communication costs in demand-aware networks.

The proposed algorithms outperform previous methods on real datacenter traffic.

Heuristic methods show reliable and efficient performance in practice.

Abstract

Emerging optical and virtualization technologies enable the design of more flexible and demand-aware networked systems, in which resources can be optimized toward the actual workload they serve. For example, in a demand-aware datacenter network, frequently communicating nodes (e.g., two virtual machines or a pair of racks in a datacenter) can be placed topologically closer, reducing communication costs and hence improving the overall network performance. This paper revisits the bounded-degree network design problem underlying such demand-aware networks. Namely, given a distribution over communicating server pairs, we want to design a network with bounded maximum degree that minimizes expected communication distance. In addition to this known problem, we introduce and study a variant where we allow Steiner nodes (i.e., additional routers) to be added to augment the network. We improve the understanding of this problem domain in several ways. First, we shed light on the complexity and hardness of the aforementioned problems, and study a connection between them and the virtual networking embedding problem. We then provide a constant-factor approximation algorithm for the Steiner node version of the problem, and use it to improve over prior state-of-the-art algorithms for the original version of the problem with sparse communication distributions. Finally, we investigate various heuristic approaches to bounded-degree network design problem, in particular providing a reliable heuristic algorithm with good experimental performance. We report on an extensive empirical evaluation, using several real-world traffic traces from datacenters, and find that our approach results in improved demand-aware network designs.