2026-09-18T17:42:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1390862022-01-15T03:40:55Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kraska, Tim
Ukyab, Tenzin Samten
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T14:49:07Z
2022-01-14T14:49:07Z
2021-06
2021-06-17T20:14:37.074Z
https://hdl.handle.net/1721.1/139086
Parallel database management systems need efficient job scheduling. Currently systems use simple heuristics ignoring the characteristics of database workloads. Therefore, we created an effective scheduler that uses machine learning techniques, such as reinforcement learning and neural networks, and does not require human intervention beyond an objective, such as reducing average job completion time. We use existing training techniques for job schedulers with dependency constraints. However, the model is specialized for database workloads using features specific to database queries, such as node operator type. In addition, we represent pipelining scheduling opportunities between operator tasks. With further training time our learned scheduler will be able to improve the average job completion time in comparison to heuristic schedulers, such as FIFO and fair scheduling.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Learned scheduling for database management systems
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>Parallel database management systems need efficient job scheduling. Currently systems use simple heuristics ignoring the characteristics of database workloads. Therefore, we created an effective scheduler that uses machine learning techniques, such as reinforcement learning and neural networks, and does not require human intervention beyond an objective, such as reducing average job completion time. We use existing training techniques for job schedulers with dependency constraints. However, the model is specialized for database workloads using features specific to database queries, such as node operator type. In addition, we represent pipelining scheduling opportunities between operator tasks. With further training time our learned scheduler will be able to improve the average job completion time in comparison to heuristic schedulers, such as FIFO and fair scheduling.</Abstract>
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