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DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
To improve the processing efficiency of jobs in distributed computing, the concept of coflow is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage computation task. A job consists of multiple coflows and can be usually formulated as a Directed-Acyclic Graph (DAG). A proper scheduling of coflows can significantly reduce the completion time of jobs in distributed computing. However, this scheduling problem is proved to be NP-hard. Different from existing
doi:10.24963/ijcai.2020/454
dblp:conf/ijcai/ZhuangLQLWH20
fatcat:olbzdcm2mvd43ic2dfryqnnzii