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Peering through the Dark: An Owl's View of Inter-job Dependencies and Jobs' Impact in Shared Clusters

Summary: Owl analyzes cluster logs to extract and visualize inter-job dependencies in shared multi-tenant infrastructures. It introduces a novel job-valuation algorithm that quantifies a job's impact on dependents and downstream users. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5809
Venue
SIGMOD
Year
2019
Pagerank
5.093636e-05
Overall Rank
11,862 | 18.62%
DOI
10.1145/3299869.3320239

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{chung_sigmod19,
        title = {{Peering through the Dark: An Owl's View of Inter-job Dependencies and Jobs' Impact in Shared Clusters}},
        author = {Chung, Andrew and Curino, Carlo and Krishnan, Subru and Karanasos, Konstantinos and Garefalakis, Panagiotis and Ganger, Gregory R.},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3320239},
        url = {https://dl.acm.org/doi/10.1145/3299869.3320239},
        year = {2019}
}

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Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
514 Goods: Organizing Google's Datasets 2016 SIGMOD 0.00017178673
1,077 DataHub: Collaborative Data Science & Dataset Version Management at Scale 2015 CIDR 0.00012269438
1,902 Ground: A Data Context Service 2017 CIDR 9.506714e-05
7,878 Dependency-Driven Analytics: a Compass for Uncharted Data Oceans 2017 CIDR 5.5246167e-05
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