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Andrew Edwards

dblp:24/1928 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2Security and privacy · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 83% Cloud and datacenter computing · 17%
Software engineering, system software, and programming languages
1 paper
Concurrent programming · 67% Program analysis · 33%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cloud storage
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Distributed systems
fault tolerance
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Distributed systems › replication
geo-replication
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Distributed systems › fault tolerance
high availability
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Distributed systems
replication
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Distributed systems › consistency models
strong consistency
0.112011
Windows Azure Storage: a highly available cloud storage service with strong consistency · SOSP 2011
Concurrent programming
concurrency bugs
0.112007
Automatically classifying benign and harmful data racesallusing replay analysis · PLDI 2007
Concurrent programming › concurrency bugs
data race classification
0.112007
Automatically classifying benign and harmful data racesallusing replay analysis · PLDI 2007
Program analysis
dynamic analysis
0.112007
Automatically classifying benign and harmful data racesallusing replay analysis · PLDI 2007
YearPublicationVenuePosition
2011 Windows Azure Storage: a highly available cloud storage service with strong consistency
abstract
Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.
Brad Calder, Ju Wang 0010, Aaron Ogus, Niranjan Nilakantan, Arild Skjolsvold, Sam McKelvie, Yikang Xu, Shashwat Srivastav, Jiesheng Wu, Huseyin Simitci, Jaidev Haridas, Chakravarthy Uddaraju, Hemal Khatri, Andrew Edwards, Vaman Bedekar, Shane Mainali, Rafay Abbasi, Mian Fahim ul Haq, Muhammad Ikram ul Haq, Deepali Bhardwaj, Sowmya Dayanand, Anitha Adusumilli, Marvin McNett, Sriram Sankaran, Kavitha Manivannan, Leonidas Rigas
SOSP14
2007 Automatically classifying benign and harmful data racesallusing replay analysis
abstract
Many concurrency bugs in multi-threaded programs are due to dataraces. There have been many efforts to develop static and dynamic mechanisms to automatically find the data races. Most of the prior work has focused on finding the data races and eliminating the false positives.
Satish Narayanasamy, Jordan Tigani, Andrew Edwards, Brad Calder
PLDI4
1996 Bolero - A TTP project for the shipping industry
Andrew Edwards
Inf. Secur. Tech. Rep.1