SpiderDAN: Matching Augmentation in Demand-Aware Networks

TL;DR

This paper introduces a new demand-aware network augmentation problem involving adding matchings to optimize shortest paths, proves its NP-hardness, and offers approximation algorithms and heuristics validated on real-world data.

Contribution

It formulates a novel demand-aware network augmentation problem, proves its NP-hardness, and develops approximation algorithms and heuristics with experimental validation.

Findings

NP-hardness of the problem even on cycles

Constant-factor approximation for demand concentrated in few nodes

Effective heuristics validated on real-world network traces

Abstract

Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication networks. In this variant, we consider a given physical network and the measured communication demands between the nodes. Our goal is to augment the given physical network with a matching, so that the shortest path lengths in the augmented network, weighted with the demands, are minimal.We prove that this problem is NP-hard, even if the physical network is a cycle. We then use results from demand-aware network design to provide a constant-factor approximation algorithm for adding a matching in case that only a few nodes in the network cause almost all the communication. For general real-world communication patterns, we design and evaluate a series of heuristics that can deal with arbitrary graphs as the underlying network structure. Our algorithms are validated experimentally using real-world traces (from e.g., Facebook) of data centers.