Allen School alumnus Ming Liu (Ph.D., ‘20) received the honorable mention for the 2021 ACM SIGCOMM Doctoral Dissertation Award for Outstanding Ph.D. Thesis in Computer Networking and Data Communication from the Association for Computing Machinery’s Special Interest Group on Data Communications. The award committee recognized Liu for “identifying and enabling novel uses of programmable network devices in data centers, including an in-network computing solution for accelerating distributed applications, and a microservice execution platform running on Smart Network Interface Cards (NICs)-accelerated servers.”
Liu’s thesis, “Building Distributed Systems Using Programmable Networks,” addresses how to best leverage emerging data center network computational elements. His aim is to help overcome stagnating CPU performance, rapid growth in network bandwidth, and the increasing cost of data transfers. His thesis makes multiple contributions using a broad range of devices spanning programmable SmartNICs, programmable switches, and network-attached accelerators to improve the performance and energy profile of cloud-based applications.
“Ming’s research could have a long-term impact on how we use programmable networks inside data centers,” said his advisor, Allen School professor Arvind Krishnamurthy. “There has been a surge in the design of programmable networking hardware, but it isn’t clear as to what problems they can solve. Ming’s work has focused on identifying novel uses of these hardware devices and enabling the pervasive use of programmable network devices in data centers. Within five years, he has made outstanding progress on this front, with many projects at the intersection of distributed systems and networking.”
Three components of Liu’s thesis advanced improvements to the performance and energy profile of cloud-based applications. IncBricks is a hardware-software co-designed system that supports caching in the network using a programmable network middlebox. With this, Liu provides significant performance benefits such as reducing end-host costs, lowering latency, and improving throughput. His iPipe project is an actor-based framework for offloading distributed applications on SmartNICs. iPipe allows a SmartNIC to be safely multi-programmed, with a real-time scheduler, process migration, and resource isolation mechanisms providing security and portability across different hardware NIC designs. Liu also developed E3, a microservice execution platform that can opportunistically move computation onto a SmartNIC to yield massive energy savings — a valuable contribution given that data centers threaten to overwhelm the nation’s power grid in the near future.
Liu, who earned his Ph.D. while working with Krishnamurthy and professor Luis Ceze, is currently a postdoctoral researcher at VMware and will join the University of Wisconsin-Madison in the fall as a professor in the computer science department.