2026-09-21T08:21:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1390972022-01-15T03:28:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stonebraker, Michael
Woicik, Matthew
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T14:49:43Z
2022-01-14T14:49:43Z
2021-06
2021-06-17T20:14:48.202Z
https://hdl.handle.net/1721.1/139097
Many cloud databases separate their compute from their storage resources. This design introduces a network bottleneck during query execution that can be mitigated through caching and computation pushdown. Depending on the environmental settings and the specific query, the amount of computation pushdown needed to achieve the optimal runtime may vary. This work presents a runtime prediction model that determines the amount of computation pushdown that results in the fastest runtime and analyzes a real-world implementation of this model on the FlexPushdownDB system running in AWS.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Determining the Optimal Amount of Computation Pushdown to Minimize Runtime for a Cloud Database
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Many cloud databases separate their compute from their storage resources. This design introduces a network bottleneck during query execution that can be mitigated through caching and computation pushdown. Depending on the environmental settings and the specific query, the amount of computation pushdown needed to achieve the optimal runtime may vary. This work presents a runtime prediction model that determines the amount of computation pushdown that results in the fastest runtime and analyzes a real-world implementation of this model on the FlexPushdownDB system running in AWS.</Abstract>
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