Locality-aware Scheduling
Locality-Aware Scheduling is a task scheduling methodology that optimizes data access efficiency by prioritizing the assignment of computational tasks to processing units in proximity to their required data, thereby minimizing data movement and leveraging spatial and temporal locality in distributed computing environments.
252
Publications
16.3K
Citations
779
Authors
305
Institutions
Publications per year
2017–2026
67
Parents
252
Mesos: a platform for fine-grained resource sharing in the data center
Benjamin Hindman, Andy Konwinski, Matei Zaharia et al. · UC Berkeley · 2011 · 1.6K citations
Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma et al. · 2010 · 1.4K citations
Michael Isard, Vijayan Prabhakaran, Jon Currey et al. · 2009 · 852 citations
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305
IBM (United States)
Armonk, United States
12 h-index
27 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
28 publications
1.5K citations
Georgia Institute of Technology
Atlanta, United States
8 h-index
17 publications
553 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
8 h-index
12 publications
1.5K citations

Pennsylvania State University
State College, United States
7 h-index
17 publications
491 citations
Arizona State University
Tempe, United States
7 h-index
21 publications
1K citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
6 h-index
15 publications
1.1K citations

Oak Ridge National Laboratory
Oak Ridge, United States
6 h-index
9 publications
572 citations
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TPDS · 9 publications · 358 citations
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FGCS · 6 publications · 271 citations
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6 publications · 233 citations
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5 publications · 114 citations
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3 publications · 86 citations
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