Founded in 2002, our laboratory conducts research on the design and implementation of a wide range of networked computing systems.
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.
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic engineering and cluster scheduling, especially at larger problem sizes. We improve both approaches: (1) we show how to convert the optimization sequence into a single fast optimization, and (2) we generalize waterfilling to the multi-path case. We empirically show our new algorithms Pareto-dominate prior techniques: they produce faster, fairer, and more efficient allocations. Some of our allocators also have theoretical guarantees: they trade off a bounded amount of unfairness for faster allocation. We have deployed our allocators in Azure’s WAN traffic engineering pipeline, where we preserve solution quality and achieve a roughly 3× speedup.
Jan 4, 2024
Four papers from the lab accepted to NSDI 2024.
Sep 27, 2023
AeroTraj has been accepted at IMWUT/UbiComp 2023.
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!
Nov 7, 2022
Our Wisden paper receives the Test of Time Award at Sensys 2022!
Sep 5, 2022
Quadrant accepted to SoCC 2022.
May 4, 2022
Sarah Cooney accepts position at Villanova University. Congrats!
April 20, 2022
Fawad Ahmad accepts position at Rochester Institute of Technology. Congrats!
April 1, 2022
CloudCluster accepted to NSDI 2022