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arXiv · 2411.11426

SpiderDAN: Matching Augmentation in Demand-Aware Networks

Aleksander Figiel·Darya Melnyk·André Nichterlein·Arash Pourdamghani·Stefan Schmid
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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.

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Aleksander Figiel, Darya Melnyk, André Nichterlein, Arash Pourdamghani, Stefan Schmid. 2024-11-18. SpiderDAN: Matching Augmentation in Demand-Aware Networks. https://arxiv.org/abs/2411.11426

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