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Dynamic Scaling for Parallel Graph Computations

Summary: Dynamic graph scaling jointly optimizes balance, replication, and migration—an intractable tri-criteria problem even for constant-scale changes. It introduces consistent-hashing approximations with guarantees and a generic wrapper that adaptively scales any vertex partitioner. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hd795ede518995e70
Venue
VLDB
Year
2019
Pagerank
4.9605343e-05
Overall Rank
12,241 | 17.95%
DOI
10.14778/3324301.3324305
PDF
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Authors

BibTeX Citation

@article{fan_vldb19,
        title = {{Dynamic Scaling for Parallel Graph Computations}},
        author = {Fan, Wenfei and Hu, Chunming and Liu, Muyang and Lu, Ping and Yin, Qiang and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {8},
        pages = {877--890},
        doi = {10.14778/3324301.3324305},
        url = {https://doi.org/10.14778/3324301.3324305},
        year = {2019}
}

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Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
644 Relational Cloud: A Database-as-a-Service for the Cloud 2011 CIDR 0.00015201984
902 Parallelizing Sequential Graph Computations 2017 SIGMOD 0.00013142001
2,391 LEOPARD: Lightweight Edge-Oriented Partitioning and Replication for Dynamic Graphs 2016 VLDB 8.5205469e-05
3,164 Energy Management for MapReduce Clusters 2010 VLDB 7.5607087e-05
3,295 A Scalable Distributed Graph Partitioner 2015 VLDB 7.4292699e-05
4,560 LogGP: A Log-based Dynamic Graph Partitioning Method 2014 VLDB 6.523673e-05
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