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Schedule Optimization for Data Processing Flows on the Cloud

Summary: Cloud dataflow scheduling as two-objective optimization (time vs cost) with a large schedule space and cloud elasticity. Prototype elastic optimizer uses greedy, probabilistic, and exhaustive search to map time/cost tradeoffs and reveal schedule traits. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hf8aca53bbd5ef177
Venue
SIGMOD
Year
2011
Pagerank
6.7464862e-05
Overall Rank
4,188 | 71.85%
DOI
10.1145/1989323.1989355

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kllapi_sigmod11,
        title = {{Schedule Optimization for Data Processing Flows on the Cloud}},
        author = {Kllapi, Herald and Sitaridi, Eva and Tsangaris, Manolis M. and Ioannidis, Yannis},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989355},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989355},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

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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
6 Pig Latin: A Not-So-Foreign Language for Data Processing 2008 SIGMOD 0.001052036
859 Query Optimization by Simulated Annealing 1987 SIGMOD 0.00013418999
1,806 Multiobjective Query Optimization 2001 PODS 9.5976083e-05
3,091 Parallel Query Scheduling and Optimization with Time- and Space-Shared Resources 1997 VLDB 7.6578995e-05
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