The UTNS research group at the University of Texas at Austin works on foundational systems and networking problems that arise in supporting big data analytics, distributed machine learning, and large-scale distributed applications. Our work explores both theoretical underpinnings and system design/implementation, and builds on ideas from adjacent areas such as game theory, optimization, and formal methods.

News

Jiaxin Lin won 2023 Google PhD Fellowship!
Darwin and Slingshot got accepted at SIGCOMM 2023!
Better Together and Shockwave got accepted at NSDI 2023!
Network Traffic Characteristics of Data Centers in the Wild (IMC, 2010) received SIGCOMM Test-of-Time Award!
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs got accepted at HOT-I 2022!
Medes and Jiffy got accepted at EuroSys 2022!

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Darwin

Flexible Learning-based CDN Caching

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Slingshot

Resilient Baseband Processing in Virtualized RANs

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A Lightweight Distributed Pre-Training Toolkit

Lowering the Pre-training Tax for Gradient-based Subset Training

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RingLeader

Efficiently Offloading Intra-Server Orchestration to NICs

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Resilient Baseband Processing in Virtualized RANs with Slingshot Nikita Lazarev, Tao Ji, Anuj Kalia, Daehyeok Kim, Ilias Marinos, Francis Y. Yan, Christina Delimitrou, Zhiru Zhang, Aditya Akella. SIGCOMM, 2023. Darwin: Flexible Learning-based CDN Caching Jiayi Chen, Nihal Sharma, Tarannum Khan, Shu Liu, Brian Chang, Aditya Akella, Sanjay Shakkottai, Ramesh Sitaraman. SIGCOMM, 2023. Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit Yeonju Ro, Zhangyang Wang, Vijay Chidambaram, Aditya Akella. ICML, 2023.

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