Towards Scalable Network Delay Minimization

TL;DR

This paper addresses the challenge of reducing network delays through scalable, data-driven node upgrade strategies, providing approximation solutions for an NP-hard problem with methods that outperform existing approaches.

Contribution

It introduces scalable, sampling-based techniques for delay minimization via node upgrades, offering the first approximation solutions for this NP-hard problem.

Findings

Methods scale almost linearly with network size

Techniques outperform competitors in delay reduction quality

Provides probabilistic approximation for a restricted problem version

Abstract

Reduction of end-to-end network delays is an optimization task with applications in multiple domains. Low delays enable improved information flow in social networks, quick spread of ideas in collaboration networks, low travel times for vehicles on road networks and increased rate of packets in the case of communication networks. Delay reduction can be achieved by both improving the propagation capabilities of individual nodes and adding additional edges in the network. One of the main challenges in such design problems is that the effects of local changes are not independent, and as a consequence, there is a combinatorial search-space of possible improvements. Thus, minimizing the cumulative propagation delay requires novel scalable and data-driven approaches. In this paper, we consider the problem of network delay minimization via node upgrades. Although the problem is NP-hard, we show that probabilistic approximation for a restricted version can be obtained. We design scalable and high-quality techniques for the general setting based on sampling and targeted to different models of delay distribution. Our methods scale almost linearly with the graph size and consistently outperform competitors in quality.