Improved Scalability of Demand-Aware Datacenter Topologies With Minimal Route Lengths and Congestion

Abstract

The performance of more and more cloud-based applications critically depends on the performance of the interconnecting datacenter network. Emerging reconfigurable datacenter networks have the potential to provide an unprecedented throughput by dynamically reconfiguring their topology in a demand-aware manner. This paper studies the algorithmic problem of how to design low-degree and hence scalable datacenter networks optimized toward the current traffic they serve. Our main contribution is a novel network design which provides asymptotically minimal route lengths and congestion. In comparison to prior work, we reduce the degree requirements by a factor of four for sparse demand matrices. We further show the problem to be already NP-hard for tree-shaped demands, but permits a 2-approximation on the route lengths and a 6-approximation for congestion. We further report on a small empirical study on Facebook traces.