Founded in 2002, our laboratory conducts research on the design and implementation of a wide range of networked computing systems.
On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.
Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heuristic and the optimum or between two heuristics in realistic scenarios. We present MetaOpt, a system that helps analyze heuristics. Users specify the heuristic and the optimal (or another heuristic) as input, and MetaOpt automatically encodes these efficiently for a solver to find performance gaps and their corresponding adversarial inputs. Its suite of built-in optimizations helps it scale its analysis to practical problem sizes. To show it is versatile, we used MetaOpt to analyze heuristics from three domains (traffic engineering, vector bin packing, and packet scheduling). We found a production traffic engineering heuristic can require 30% more capacity than the optimal to satisfy realistic demands. Based on the patterns in the adversarial inputs MetaOpt produced, we modified the heuristic to reduce its performance gap by 12.5×. We examined adversarial inputs to a vector bin packing heuristic and proved a new lower bound on its performance.
Sep 1, 2024
Weiwu Pang joins Google Cloud NetInfra. Congrats!
July 24, 2024
Three papers accepted to NSDI 2025 (Spring).
June 21, 2024
RECACP accepted to Mobicom 2024..
Jan 4, 2024
Four papers accepted to NSDI 2024.
Oct 1, 2023
Jane Yen joins Google Cloud NetInfra Team. Congrats!
Sep 27, 2023
AeroTraj accepted to IMWUT/UbiComp 2023.
July 1, 2023
Jianfeng Wang joins Oracle. Congrats!
Jul 1, 2023
Hang Qiu joins University of California, Riverside as an Assistant Professor. Congrats!
May 30, 2023
Our lab's work on estimating energy usage of Android apps receives the Impact Award at ISSTA 2023!