Siddhant Ray

Selected publications

1. NSDI24

   Transformer-based Predictions for Sudden Network Changes (Poster)

   Siddhant Ray, Xi Jiang, Zhuohan Guo, Junchen Jiang, and Nick Feamster

   In 21st USENIX Symposium on Networked Systems Design and Implementation 2024

   Abs Bib PDF

   Accurate predictions on sudden changes in network states are crucial for the integrity of real-time applications. Traditional heuristic models fall short, especially in tail cases, struggling to capture long-term network dependencies. Modelling network traces as time series sequences, we explore the use of a Transformer model architecture, known for its success in time series prediction, to model network trace dependencies, focusing on sudden change predictions. Our preliminary result on using a Transformer model for predicting one-way delay (OWD) shows observable improvement over the heuristic baseline in prediction loss. This suggests a promising direction for enhancing network predictability and optimizing resource utilization.

   @inproceedings{siddhantnsdi,
     author = {Ray, Siddhant and Jiang, Xi and Guo, Zhuohan and Jiang, Junchen and Feamster, Nick},
     title = {Transformer-based Predictions for Sudden Network Changes (Poster)},
     year = {2024},
     isbn = {9781450398992},
     publisher = {USENIX Association},
     booktitle = {21st USENIX Symposium on Networked Systems Design and Implementation},
     keywords = {packet-level modeling, transformer},
     location = {Santa Clara, CA},
     series = {NSDI '24},
   }

2. HotNets22

   A New Hope for Network Model Generalization

   Alexander Dietmüller, Siddhant Ray, Romain Jacob, and Laurent Vanbever

   In Proceedings of the 21st ACM Workshop on Hot Topics in Networks 2022

   Abs arXiv Bib Code

   Generalizing machine learning (ML) models for network traffic dynamics tends to be considered a lost cause. Hence, for every new task, we often resolve to design new models and train them on model-specific datasets collected, whenever possible, in an environment mimicking the model’s deployment. This approach essentially gives up on generalization. Yet, an ML architecture called_Transformer_ has enabled previously unimaginable generalization in other domains. Nowadays, one can download a model pre-trained on massive datasets and only fine-tune it for a specific task and context with comparatively little time and data. These fine-tuned models are now state-of-the-art for many benchmarks. We believe this progress could translate to networking and propose a Network Traffic Transformer (NTT), a transformer adapted to learn network dynamics from packet traces. Our initial results are promising: NTT seems able to generalize to new prediction tasks and contexts. This study suggests there is still hope for generalization, though it calls for a lot of future research.

   @inproceedings{dietmuller2022new,
     author = {Dietm\"{u}ller, Alexander and Ray, Siddhant and Jacob, Romain and Vanbever, Laurent},
     title = {A New Hope for Network Model Generalization},
     year = {2022},
     isbn = {9781450398992},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3563766.3564104},
     doi = {10.1145/3563766.3564104},
     booktitle = {Proceedings of the 21st ACM Workshop on Hot Topics in Networks},
     pages = {152–159},
     numpages = {8},
     keywords = {packet-level modeling, transformer},
     location = {Austin, Texas},
     series = {HotNets '22},
   }