{"publication":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","conference":{"end_date":"2021-01-12","name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","start_date":"2021-01-09","location":"Las Vegas, USA"},"citation":{"bibtex":"@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>}, booktitle={2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }","ama":"Hasnain A, Karl H. Learning Flow Scheduling. In: <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society. doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","short":"A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC), IEEE Computer Society, n.d.","ieee":"A. Hasnain and H. Karl, “Learning Flow Scheduling,” in <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, Las Vegas, USA.","apa":"Hasnain, A., &#38; Karl, H. (n.d.). Learning Flow Scheduling. In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. Las Vegas, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","mla":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, IEEE Computer Society, doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","chicago":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society, n.d. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>."},"title":"Learning Flow Scheduling","_id":"20125","date_updated":"2022-01-06T06:54:20Z","ddc":["000"],"abstract":[{"text":"Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.","lang":"eng"}],"doi":"https://doi.org/10.1109/CCNC49032.2021.9369514","type":"conference","user_id":"63288","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9369514"}],"language":[{"iso":"eng"}],"publisher":"IEEE Computer Society","publication_status":"accepted","date_created":"2020-10-19T14:27:17Z","keyword":["Flow scheduling","Deadlines","Reinforcement learning"],"year":"2021","project":[{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"},{"_id":"1","name":"SFB 901"}],"author":[{"id":"63288","last_name":"Hasnain","full_name":"Hasnain, Asif","first_name":"Asif"},{"first_name":"Holger","full_name":"Karl, Holger","last_name":"Karl","id":"126"}],"status":"public","department":[{"_id":"75"}]}