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David G. Andersen
Researcher at Carnegie Mellon University
Publications - 158
Citations - 19459
David G. Andersen is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: The Internet & Network packet. The author has an hindex of 60, co-authored 157 publications receiving 17855 citations. Previous affiliations of David G. Andersen include Intel & Massachusetts Institute of Technology.
Papers
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Proceedings ArticleDOI
Resilient overlay networks
TL;DR: It is found that forwarding packets via at most one intermediate RON node is sufficient to overcome faults and improve performance in most cases, demonstrating the benefits of moving some of the control over routing into the hands of end-systems.
Journal ArticleDOI
Resilient overlay networks
TL;DR: It is found that forwarding packets via at most one intermediate RON node is sufficient to overcome faults and improve performance in most cases, demonstrating the benefits of moving some of the control over routing into the hands of end-systems.
Proceedings ArticleDOI
Scaling distributed machine learning with the parameter server
Mu Li,David G. Andersen,Jun Woo Park,Alexander J. Smola,Amr Ahmed,Vanja Josifovski,James Long,Eugene J. Shekita,Bor-Yiing Su +8 more
TL;DR: In this paper, the authors propose a parameter server framework for distributed machine learning problems, where both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices.
Proceedings ArticleDOI
c-Through: part-time optics in data centers
Guohui Wang,David G. Andersen,Michael Kaminsky,Konstantina Papagiannaki,T. S. Eugene Ng,Michael Kozuch,Michael P. Ryan +6 more
TL;DR: This work proposes a hybrid packet and circuit switched data center network architecture (or HyPaC) which augments the traditional hierarchy of packet switches with a high speed, low complexity, rack-to-rack optical circuit-switched network to supply high bandwidth to applications.
Proceedings ArticleDOI
Don't settle for eventual: scalable causal consistency for wide-area storage with COPS
TL;DR: This paper identifies and defines a consistency model---causal consistency with convergent conflict handling, or causal+---that is the strongest achieved under these constraints and presents the design and implementation of COPS, a key-value store that delivers this consistency model across the wide-area.