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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
- 4390
- Venue
- SIGMOD
- Year
- 2011
- Pagerank
- 5.9882572e-05
- Overall Rank
- 4,700 | 67.31%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,659 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
8.3604734e-05 |
| 3,710 |
Optimizing Analytic Data Flows for Multiple Execution Engines |
2012 |
SIGMOD |
6.8238962e-05 |
| 4,874 |
Approximation Schemes for Many-Objective Query Optimization |
2014 |
SIGMOD |
5.8594632e-05 |
| 5,368 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5457532e-05 |
| 7,889 |
Cost-Intelligent Data Analytics in the Cloud |
2024 |
CIDR |
4.6253386e-05 |
| 8,615 |
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual" |
2021 |
VLDB |
4.484683e-05 |
| 8,617 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
4.4846425e-05 |
| 8,725 |
A Fast Randomized Algorithm for Multi-Objective Query Optimization |
2016 |
SIGMOD |
4.4600243e-05 |
| 9,123 |
External Merge Sort for Top-K Queries: Eager input filtering guided by histograms |
2020 |
SIGMOD |
4.3920263e-05 |
| 9,305 |
Parallelizing Query Optimization on Shared-Nothing Architectures |
2016 |
VLDB |
4.3577129e-05 |
| 10,259 |
Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink |
2026 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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