Xiaoqi (Danny) Chen will present his FPO "Designing Compact Data Structures for Network Measurement and Control" on Wednesday, October 4, 2023 at 2pm in Friend Center 125 (and Zoom)
Xiaoqi (Danny) Chen will present his FPO "Designing Compact Data Structures for Network Measurement and Control" on Wednesday, October 4, 2023 at 2pm in Friend Center 125 (and Zoom). Zoom link: [ https://princeton.zoom.us/j/96617518355 | https://princeton.zoom.us/j/96617518355 ] The members of his committee are as follows: Examiners: Jennifer Rexford (adviser), David Walker, and Maria Apostolaki Readers: Mark Braverman and Minlan Yu (Harvard University) Talk abstract follows below. All are welcome to join. Abstract: This dissertation explores the implementation of network measurement and closed-loop control in the data plane of high-speed programmable switches. After discussing the algorithmic constraints imposed by the switch pipeline architecture, primarily stemming from the requirement of high-speed processing, we share our experience in tailoring algorithms for the data plane. Initially, we focus on efficient measurement algorithms, and present two works for detecting heavy hitters and executing multiple distinct-count queries; both require designing novel approximate data structures to meet the tight memory access constraints. Subsequently, we pivot towards using real-time, closed-loop control in the data plane for performance optimization, and present two works for mitigating microbursts and enforcing fair bandwidth limits; both require approximated computation and exploit the sub-millisecond reaction latency unattainable through conventional control planes. We hope by sharing our experience and techniques, which are widely applicable to various algorithms and other data-plane hardware targets, we can lay the foundation for future innovations in the field of network programming for researchers and practitioners alike.
Minsung Kim will present his FPO "Quantum and Quantum-Inspired Computation for Wireless Networks" on Tuesday, 10/3/2023 at 2pm in CS 302. The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Jennifer Rexford, and Yasaman Ghasempour Readers: Ravi Netravali, Lin Zhong (Yale), and Davide Venturelli (NASA/USRA) Talk abstract follows below. All are welcome to join. Abstract: A central design challenge for future generations of wireless networks is to meet the ever-increasing demand for wireless capacity. While significant progress has been made in designing advanced wireless technologies, the current computational capacity at base stations to support them has been consistently identified as the bottleneck, due to limitations in processing time. Quantum computing is a potential tool to overcome the tradeoff between the wireless performance and computational complexity. It exploits unique information processing capabilities based on quantum mechanics to perform fast calculations that are intractable by traditional digital methods. This dissertation presents four design directions of quantum compute-enabled wireless systems to expedite baseband processing at base stations, which would unlock unprecedented levels of wireless performance in telecommunication networks: (1) quantum optimization on specialized hardware, (2) quantum-inspired computing on classical computing platforms, (3) hybrid classical-quantum computational structures, and (4) scalable and elastic parallel quantum optimization. For the directions, we introduce our prototype systems (QuAMax, ParaMax, IoT-ResQ, X-ResQ) that are implemented on real-world quantum processors. The prototypes are designed for quantum-accelerated near-optimal wireless signal processing in Multiple-Input Multiple-Output (MIMO) systems that could drastically increase wireless capacity for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. We provide design guidance in the systems with underlying principles and technical details, and discuss future research directions based on the current challenges and opportunities observed.
participants (2)
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Gradinfo -
Nicki Mahler