Learning scheduling algorithms for data processing clusters
© 2019 Association for Computing Machinery. Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems use simple, generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each worklo...
Main Authors: | Mao, Hongzi, Schwarzkopf, Malte, Venkatakrishnan, Shaileshh Bojja, Meng, Zili, Alizadeh, Mohammad |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Association for Computing Machinery (ACM)
2021
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Online Access: | https://hdl.handle.net/1721.1/137428 |
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