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Deb Agarwal

Researcher at Lawrence Berkeley National Laboratory

Publications -  104
Citations -  3731

Deb Agarwal is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Data management & The Internet. The author has an hindex of 25, co-authored 100 publications receiving 3054 citations. Previous affiliations of Deb Agarwal include University of California, Santa Barbara.

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Journal ArticleDOI

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Gilberto Pastorello, +303 more
- 09 Jul 2020 - 
TL;DR: The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe, and is detailed in this paper.
Journal ArticleDOI

Totem: a fault-tolerant multicast group communication system

TL;DR: When Totem delivers multicast messages, it invokes operations in the same total order throughout the distributed system, resulting in consistency of replicated data and simpli ed programming of applications.
Journal ArticleDOI

The Totem single-ring ordering and membership protocol

TL;DR: An effective flow control mechanism enables the Totem single-ring protocol to achieve message-ordering rates significantly higher than the best prior total-ordering protocols.
Proceedings ArticleDOI

Extended virtual synchrony

TL;DR: A model of extended virtual synchrony is formulated that defines a group communication transport service for multicast and broadcast communication in a distributed system and provides well-defined self-delivery and failure atomicity properties.
Proceedings ArticleDOI

eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform

TL;DR: This paper presents the design and implementation of a MODIS satellite data reprojection and reduction pipeline in the Windows Azure cloud computing platform, and shows that by running a practical large-scale science data processing job in the pipeline, the application was able to produce analytical results in nearly 90× less time than was possible with a high-end desktop machine.