Yue Cheng

selected publications

1. USENIX FAST’23

   SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training

   Redwan Ibne Seraj Khan, Ahmad Hossein Yazdani, Yuqi Fu, Arnab K. Paul, Bo Ji, Xun Jian,  Yue Cheng,  and Ali R. Butt

   In 21th USENIX Conference on File and Storage Technologies (FAST 23) 2023
2. SC’22

   SFS: Smart OS Scheduling for Serverless Functions

   Yuqi Fu, Li Liu, Haoliang Wang,  Yue Cheng,  and Songqing Chen

   In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis 2022

   Best Student Paper Award Finalist
   arXiv PDF Code
3. USENIX ATC’21

   FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute

   Ao Wang, Shuai Chang, Huangshi Tian, Hongqi Wang, Haoran Yang, Huiba Li, Rui Du,  and Yue Cheng

   In 2021 USENIX Annual Technical Conference (USENIX ATC 21) 2021

   PDF Code
4. SoCC’20

   Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing

   Benjamin Carver, Jingyuan Zhang, Ao Wang, Ali Anwar, Panruo Wu,  and Yue Cheng

   In Proceedings of the 11th ACM Symposium on Cloud Computing 2020

   Abstract arXiv PDF Code Talk

   Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major challenge for serverless execution frameworks, which will need to rapidly scale and schedule tasks at high throughput, while minimizing data movement across tasks. We demonstrate that, for serverless parallel computations, decentralized scheduling enables scheduling to be distributed across Lambda executors that can schedule tasks in parallel, and brings multiple benefits, including enhanced data locality, reduced network I/Os, automatic resource elasticity, and improved cost effectiveness. We describe the implementation and deployment of our new serverless parallel framework, called Wukong, on AWS Lambda. We show that Wukong achieves near-ideal scalability, executes parallel computation jobs up to 68.17X faster, reduces network I/O by multiple orders of magnitude, and achieves 92.96% tenant-side cost savings compared to numpywren.

5. HPDC’20

   TiFL: A Tier-Based Federated Learning System

   Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan,  and Yue Cheng

   In Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing 2020

   Abstract arXiv PDF

   Federated Learning (FL) enables learning a shared model acrossmany clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data among different clients. We conduct a case study to show that heterogeneity in resource and data has a significant impact on training time and model accuracy in conventional FL systems. To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource anddata quantity. To further tame the heterogeneity caused by non-IID (Independent and Identical Distribution) data and resources, TiFL employs an adaptive tier selection approach to update the tiering on-the-fly based on the observed training performance and accuracy. We prototype TiFL in a FL testbed following Google’s FL architecture and evaluate it using the state-of-the-art FL benchmarks. Experimental evaluation shows that TiFL outperforms the conventional FL in various heterogeneous conditions. With the proposed adaptive tier selection policy, we demonstrate that TiFL achieves much faster training performance while achieving the same or better test accuracy across the board.

6. USENIX FAST’20

   InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache

   Ao Wang, Jingyuan Zhang, Xiaolong Ma, Ali Anwar, Lukas Rupprecht, Dimitrios Skourtis, Vasily Tarasov, Feng Yan,  and Yue Cheng

   In 18th USENIX Conference on File and Storage Technologies (FAST 20) 2020

   PDF Talk Website Press Blog
7. USENIX FAST’18

   Improving Docker Registry Design Based on Production Workload Analysis

   Ali Anwar, Mohamed Mohamed, Vasily Tarasov, Michael Littley, Lukas Rupprecht,  Yue Cheng, Nannan Zhao, Dimitrios Skourtis, Amit S. Warke, Heiko Ludwig, Dean Hildebrand,  and Ali R. Butt

   In 16th USENIX Conference on File and Storage Technologies (FAST 18) 2018

   PDF Code Talk
8. USENIX ATC’16

   Erasing Belady’s Limitations: In Search of Flash Cache Offline Optimality

   Yue Cheng, Fred Douglis, Philip Shilane, Grant Wallace, Peter Desnoyers,  and Kai Li

   In 2016 USENIX Annual Technical Conference (USENIX ATC 16) 2016

   PDF Talk