LASLiN: A Learning-Augmented Peer-to-Peer Network

2025-09-15

AI Summary

This paper addresses the challenge of predictable yet uncertain traffic patterns in P2P networks by proposing a demand-aware topology optimization method: minimizing the communication-demand-weighted average path length while formally bounding both routing path stretch and maximum node degree. To this end, we design LASLiN—a learning-augmented protocol that integrates continuous hierarchical abstraction modeling, dynamic programming-based optimization, and an extension of the Uniform P2P protocol to construct a provably robust static skip graph. Theoretically, LASLiN achieves near-optimal static skip graph performance under perfect demand prediction; under completely erroneous predictions, its performance degrades by at most a logarithmic factor—O(log n)—significantly outperforming existing approaches, especially in sparse-demand scenarios.

Technology Category

Search and Optimization: Learning to SearchPlanning, Routing, and Scheduling: Scheduling under UncertaintyConstraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsSocial Networks and Social Media: Social mining and social search on the Web

Abstract

We introduce a learning-augmented peer-to-peer (P2P) network design that leverages the predictions of traffic patterns to optimize the network's topology. While keeping formal guarantees on the standard P2P metrics (routing path length, maximum degree), we optimize the network in a demand-aware manner and minimize the path lengths weighted by the peer-to-peer communication demands. Our protocol is learning-augmented, meaning that each node receives an individual, possibly inaccurate prediction about the future traffic patterns, with the goal of improving the network's performances. We strike a trade-off between significantly improved performances when the predictions are correct (consistency) and polylogarithmic performances when the predictions are arbitrary (robustness). We have two main contributions. First, we consider the centralized setting and show that the problem of constructing an optimum static skip list network (SLN) is solvable in polynomial time and can be computed via dynamic programming. This problem is the natural demand-aware extension of the optimal skip list problem. Second, we introduce the Uniform P2P protocol which generalizes skip list networks (SLN) by relaxing the node's heights from discrete to continuous. We show that Uniform achieves state-of-the-art performances: logarithmic routing and maximum degree, both with high probability. We then use Uniform to build a learning-augmented P2P protocol in order to incorporate demand-awareness, leading to our main contribution, LASLiN. We prove that the performances of LASLiN are consistent with those of an optimum static SLN with correct predictions (given via our dynamic programming approach), and are at most a logarithmic factor off the state-of-the-art P2P protocols if the predictions are arbitrary wrong. For the special case of highly sparse demands, we show that LASLiN achieves improved performances.

Problem

• Optimizing P2P network topology using traffic predictions
• Balancing performance consistency with prediction accuracy
• Developing learning-augmented protocol with formal guarantees

Innovation

• Learning-augmented P2P network with traffic predictions
• Dynamic programming for optimal static skip list
• Continuous node heights for logarithmic routing performance