Improving networked-computing efficiencies
Computers now operate in a connected, often mobile world. Our research into operating systems, networks, and distributed computing is focused on developing technologies that enable computers to operate more effectively in a networked environment, and that provide the infrastructure required to enable the deployment, operation, management, and security of distributed applications.
Youshan Miao, Wentao Han, Kaiwei Li, Ming Wu, Fan Yang, Lidong Zhou, Vijayan Prabhakaran, Enhong Chen, and Wenguang Chen, ImmortalGraph: A System for Storage and Analysis of Temporal Graphs, in ACM Transactions on Storage (TOS), ACM – Association for Computing Machinery, December 2015.
Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, and Eric Chung, Toward Accelerating Deep Learning at Scale Using Specialized Logic, HOTCHIPS, August 2015.
Feng Yan, Olatunji Ruwase, Yuxiong He, and Trishul Chilimbi, Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems, KDD, August 2015.
Hitesh Ballani, Paolo Costa, Christos Gkantsidis, Matthew P. Grosvenor, Thomas Karagiannis, Lazaros Koromilas, and Greg O'Shea, Enabling End-host Network Functions, in SIGCOMM, ACM – Association for Computing Machinery, August 2015.
Paolo Costa, Hitesh Ballani, Kaveh Razavi, and Ian Kash, R2C2: A Network Stack for Rack-scale Computers, in SIGCOMM 2015, ACM – Association for Computing Machinery, August 2015.
- Microsoft Research Storage Toolkit
- Scalable Hyperlink Store
- Project Hawaii SDK for Android
- Project Hawaii SDK
- Mobility and Networking Research
- Security and Privacy Research
- Systems Research Group - Redmond
- Systems Research Group - Asia
- Wireless and Networking
- Robust Distributed System Nucleus (rDSN)
- Graph Engine
- Compression Accelerators
- Ziria - Wireless Programming for Hardware Dummies
- MODIST: Transparent Model Checking of Unmodified Cloud Systems
- Rack-scale Computing
- Software-driven wide area networks
- Face In The Crowd
- Human-Building Analytics
- Contextual Fuzzing for Mobile App Testing