@inproceedings{21005,
  abstract     = {{Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering.  LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.}},
  author       = {{Hasnain, Asif and Karl, Holger}},
  booktitle    = {{IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}},
  keywords     = {{Coflow scheduling, Reinforcement learning, Deadlines}},
  location     = {{Vancouver BC Canada}},
  publisher    = {{IEEE Communications Society}},
  title        = {{{Learning Coflow Admissions}}},
  doi          = {{10.1109/INFOCOMWKSHPS51825.2021.9484599}},
  year         = {{2021}},
}