Our mission is to develop large-scale optimization techniques and use them to improve the efficiency and robustness of infrastructure at Google.
Background
We apply techniques from areas such as combinatorial optimization, online algorithms, and control theory to make Google’s big computational infrastructure do more with less. We combine online and offline optimizations to achieve such goals as increasing throughput, decreasing latency, minimizing resource contention, maximizing the efficacy of caches, and eliminating unnecessary work in distributed systems. Our research is used in critical infrastructure that supports products such as Search and Cloud.
Some of our projects
Some of our publications
arXiv (2016) (to appear)
29th ACM Symposium on Parallelism in Algorithms and Architectures (2017)
NIPS, Neural Information Processing Systems Foundation (2014)
ACM Transactions on Algorithms, vol. 9 (4) (2013)
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (2018), pp. 587-604