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Anthony D. Joseph
Researcher at University of California, Berkeley
Publications - 191
Citations - 37579
Anthony D. Joseph is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: The Internet & Information system. The author has an hindex of 53, co-authored 188 publications receiving 35421 citations. Previous affiliations of Anthony D. Joseph include Intel & University of California.
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Journal ArticleDOI
A view of cloud computing
Michael Armbrust,Armando Fox,Rean Griffith,Anthony D. Joseph,Randy H. Katz,Andy Konwinski,Gunho Lee,David A. Patterson,Ariel Rabkin,Ion Stoica,Matei Zaharia +10 more
TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Journal Article
Above the Clouds: A Berkeley View of Cloud Computing
Michael Armbrust,Armando Fox,Rean Griffith,Anthony D. Joseph,Randy H. Katz,Andy Konwinski,Gunho Lee,David A. Patterson,Ariel Rabkin,Ion Stoica,Matei Zaharia +10 more
TL;DR: This work focuses on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SAAS Users, and uses the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public.
Tapestry: An Infrastructure for Fault-tolerant Wide-area Location and Routing
TL;DR: Tapestry is an overlay location and routing infrastructure that provides location-independent routing of messages directly to the closest copy of an object or service using only point-to-point links and without centralized resources.
Journal ArticleDOI
Tapestry: a resilient global-scale overlay for service deployment
TL;DR: Experimental results show that Tapestry exhibits stable behavior and performance as an overlay, despite the instability of the underlying network layers, illustrating its utility as a deployment infrastructure.
Proceedings ArticleDOI
Improving MapReduce performance in heterogeneous environments
TL;DR: A new scheduling algorithm, Longest Approximate Time to End (LATE), that is highly robust to heterogeneity and can improve Hadoop response times by a factor of 2 in clusters of 200 virtual machines on EC2.