# Harnessing Generative Pre-Trained Transformer for Datacenter Packet Trace Generation

arXiv: https://arxiv.org/abs/2501.12033 · HTML: https://mlsanity.com/paper/2501.12033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN](https://mlsanity.com/paper/2111.09509/) (2021)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [Flow-based Network Traffic Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1810.07795/) (2018)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [High-Fidelity Cellular Network Control-Plane Traffic Generation without Domain Knowledge](https://mlsanity.com/paper/2411.07345/) (2024)
- [Demand-Aware Network Designs of Bounded Degree](https://mlsanity.com/paper/1705.06024/) (2017)
- [Toward Demand-Aware Networking: A Theory for Self-Adjusting Networks](https://mlsanity.com/paper/1807.02935/) (2018)

## Abstract (arXiv)

Today, the rapid growth of applications reliant on datacenters calls for new advancements to meet the increasing traffic and computational demands. Traffic traces from datacenters are essential for further development and optimization of future datacenters. However, traces are rarely released to the public. Researchers often use simplified mathematical models that lack the depth needed to recreate intricate traffic patterns and, thus, miss optimization opportunities found in realistic traffic. In this preliminary work, we introduce DTG-GPT, a packet-level Datacenter Traffic Generator (DTG), based on the generative pre-trained transformer (GPT) architecture used by many state-of-the-art large language models. We train our model on a small set of available traffic traces from different domains and offer a simple methodology to evaluate the fidelity of the generated traces to their original counterparts. We show that DTG-GPT can synthesize novel traces that mimic the spatiotemporal patterns found in real traffic traces. We further demonstrate that DTG-GPT can generate traces for networks of different scales while maintaining fidelity. Our findings indicate the potential that, in the future, similar models to DTG-GPT will allow datacenter operators to release traffic information to the research community via trained GPT models.