# REPETITA: Repeatable Experiments for Performance Evaluation of Traffic-Engineering Algorithms

arXiv: https://arxiv.org/abs/1710.08665 · HTML: https://mlsanity.com/paper/1710.08665/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [Graph Neural Modeling of Network Flows](https://mlsanity.com/paper/2209.05208/) (2022)
- [Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics](https://mlsanity.com/paper/2410.10377/) (2024)
- [Optimizing Resource Allocation for Geographically-Distributed Inference by Large Language Models](https://mlsanity.com/paper/2512.21884/) (2025)
- [Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms](https://mlsanity.com/paper/2604.02927/) (2026)

## Abstract (arXiv)

In this paper, we propose a pragmatic approach to improve reproducibility of experimental analyses of traffic engineering (TE) algorithms, whose implementation, evaluation and comparison are currently hard to replicate. Our envisioned goal is to enable universally-checkable experiments of existing and future TE algorithms. We describe the design and implementation of REPETITA, a software framework that implements common TE functions, automates experimental setup, and eases comparisons (in terms of solution quality, execution time, etc.) of TE algorithms. In its current version, REPETITA includes (i) a dataset for repeatable experiments, consisting of more than 250 real network topologies with complete bandwidth and delay information as well as associated traffic matrices; and (ii) the implementation of state-of-the-art algorithms for intra-domain TE with IGP weight tweaking and Segment Routing optimization. We showcase how our framework can successfully reproduce results described in the literature, and ease new analyses of qualitatively-diverse TE algorithms. We publicly release our REPETITA implementation, hoping that the community will consider it as a demonstration of feasibility, an incentive and an initial code basis for improving experiment reproducibility: Its plugin-oriented architecture indeed makes REPETITA easy to extend with new datasets, algorithms, TE primitives and analyses. We therefore invite the research community to use and contribute to our released code and dataset.