Cost-Effective, Workload-Adaptive Migration of Big Data Applications to the Cloud
Summary: Cost-effective, workload-adaptive migration of large, multi-system big data stacks to the cloud. Goal-driven framework with concrete system architecture, cloud-mapping options, and a demonstration script showing real-world use cases to plan, design, and validate cloud adoption across on-prem, private, public, and hybrid deployments. (summarized by gpt-5-nano on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 868 | Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs | 2011 | VLDB | 0.00015789681 |
| 2,234 | PerfEnforce Demonstration: Data Analytics with Performance Guarantees | 2016 | SIGMOD | 9.2272296e-05 |
| 3,216 | WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases | 2016 | VLDB | 7.3601267e-05 |
| 4,961 | Releasing Cloud Databases from the Chains of Performance Prediction Models | 2017 | CIDR | 5.7984657e-05 |
| 11,635 | Automated Performance Management for the Big Data Stack | 2019 | CIDR | 4.1945683e-05 |
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