Demand-Aware Network Designs of Bounded Degree

TL;DR

This paper studies demand-aware network designs that adapt to communication patterns, focusing on bounded-degree networks to minimize expected path length, and provides algorithms for specific distribution types.

Contribution

It introduces the demand-aware network design problem with degree bounds, establishes lower bounds based on entropy, and offers asymptotically optimal algorithms for certain distribution families.

Findings

Lower bounds derived from communication entropy.

Optimal algorithms for sparse and locally bounded doubling distributions.

Connections established between network design, combinatorics, and information theory.

Abstract

Traditionally, networks such as datacenter interconnects are designed to optimize worst-case performance under arbitrary traffic patterns. Such network designs can however be far from optimal when considering the actual workloads and traffic patterns which they serve. This insight led to the development of demand-aware datacenter interconnects which can be reconfigured depending on the workload. Motivated by these trends, this paper initiates the algorithmic study of demand-aware networks (DANs) designs, and in particular the design of bounded-degree networks. The inputs to the network design problem are a discrete communication request distribution, D, defined over communicating pairs from the node set V , and a bound, d, on the maximum degree. In turn, our objective is to design an (undirected) demand-aware network N = (V,E) of bounded-degree d, which provides short routing paths between frequently communicating nodes distributed across N. In particular, the designed network should minimize the expected path length on N (with respect to D), which is a basic measure of the efficiency of the network. We show that this fundamental network design problem exhibits interesting connections to several classic combinatorial problems and to information theory. We derive a general lower bound based on the entropy of the communication pattern D, and present asymptotically optimal network-aware design algorithms for important distribution families, such as sparse distributions and distributions of locally bounded doubling dimensions.