Straggler Mitigation
Straggler Mitigation is a distributed data processing technique that specifically addresses the delay caused by slow-running tasks by employing strategies such as task replication, speculative execution, or dynamic resource allocation to ensure timely completion of parallel computations.
175
Publications
9K
Citations
489
Authors
185
Institutions
Publications per year
2017–2026
133
175
Speeding Up Distributed Machine Learning Using Codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani et al. · IEEE Transactions on Information Theory · 2017 · 866 citations · Full text
Effective straggler mitigation: attack of the clones
Ganesh Ananthanarayanan, Ali Ghodsi, Scott Shenker et al. · 2013 · 415 citations
YongChul Kwon, Magdalena Bałazińska, Bill Howe et al. · 2012 · 399 citations
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad et al. · 2020 · 313 citations
Faraz S. Ahmad, Srimat Chakradhar, Anand Raghunathan et al. · 2012 · 229 citations
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185
University of Southern California
Los Angeles, United States
11 h-index
26 publications
2K citations
University of California, Berkeley
Berkeley, United States
8 h-index
26 publications
4.6K citations

Beihang University
Beijing, China
6 h-index
12 publications
397 citations

University of Leeds
Leeds, United Kingdom
6 h-index
21 publications
582 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
18 publications
1.3K citations
Purdue University West Lafayette
West Lafayette, United States
6 h-index
12 publications
1K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
6 h-index
13 publications
1.1K citations
Microsoft (United States)
Redmond, United States
5 h-index
5 publications
490 citations
1–8 of 185
Repository · 25 publications · 965 citations
19
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6 publications · 212 citations
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5 publications · 201 citations
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TPDS · 5 publications · 185 citations
5
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3 publications · 170 citations
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