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Rodrigo Fonseca
Researcher at Brown University
Publications - 116
Citations - 8356
Rodrigo Fonseca is an academic researcher from Brown University. The author has contributed to research in topics: Cloud computing & The Internet. The author has an hindex of 36, co-authored 107 publications receiving 7506 citations. Previous affiliations of Rodrigo Fonseca include George Mason University & Microsoft.
Papers
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Proceedings ArticleDOI
Collection tree protocol
TL;DR: In this paper, the authors evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol, on both interference-free and interference-prone channels.
Proceedings Article
X-trace: a pervasive network tracing framework
TL;DR: This paper proposes X-Trace, a tracing framework that provides such a comprehensive view of service behavior for systems that adopt it, and discusses how it works in three deployed scenarios: DNS resolution, a three-tiered photo-hosting website, and a service accessed through an overlay network.
Proceedings Article
Four-Bit Wireless Link Estimation.
TL;DR: A link estimator design with narrow, protocol-independent interfaces for the layers is presented that reduces packet delivery costs by up to 44% over current approaches and maintains a 99% delivery ratio over large, multihop testbeds.
Proceedings ArticleDOI
Beacon vector routing: scalable point-to-point routing in wireless sensornets
Rodrigo Fonseca,Sylvia Ratnasamy,Jerry Zhao,Cheng Tien Ee,David E. Culler,Scott Shenker,Ion Stoica +6 more
TL;DR: This work proposes a practical and scalable technique for point-to-point routing in wireless sensornets, called Beacon Vector Routing, which assigns coordinates to nodes based on the vector of hop count distances to a small set of beacons, and then defines a distance metric on these coordinates.
Proceedings ArticleDOI
Jockey: guaranteed job latency in data parallel clusters
TL;DR: Jockey provides latency SLOs for data parallel jobs written in SCOPE and dynamically adjusts resource allocation in the shared cluster in order to maximize the job's economic utility while minimizing its impact on the rest of the cluster.