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Pixida: Optimizing Data Parallel Jobs in Wide-Area Data Analytics

Summary: PIXIDA schedules wide-area data-parallel jobs to minimize traffic over constrained, volatile cross-DC links. Its SILO abstraction enables a job-specific graph-partitioning formulation and algorithm, integrated with Spark, reducing cross-DC traffic up to 9×. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11548
Venue
VLDB
Year
2016
Pagerank
5.967889e-05
Overall Rank
6,124 | 57.99%
DOI
10.14778/2850578.2850581

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kloudas_vldb16,
        title = {{Pixida: Optimizing Data Parallel Jobs in Wide-Area Data Analytics}},
        author = {Kloudas, Konstantinos and Mamede, Margarida and Preguica, Nuno and Rodrigues, Rodrigo},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {2},
        pages = {72--83},
        doi = {10.14778/2850578.2850581},
        url = {https://doi.org/10.14778/2850578.2850581},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,582 Yugong: Geo-Distributed Data and Job Placement at Scale 2019 VLDB 6.1619082e-05
11,536 AutoMon: Automatic Distributed Monitoring for Arbitrary Multivariate Functions 2022 SIGMOD 5.093636e-05
12,050 The Challenges of Global-scale Data Management 2016 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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