[{"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9369514"}],"type":"conference","citation":{"ieee":"A. Hasnain and H. Karl, “Learning Flow Scheduling,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, USA.","short":"A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer Society, n.d.","mla":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer Society, doi:https://doi.org/10.1109/CCNC49032.2021.9369514.","bibtex":"@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={https://doi.org/10.1109/CCNC49032.2021.9369514}, booktitle={2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }","chicago":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE Computer Society, n.d. https://doi.org/10.1109/CCNC49032.2021.9369514.","ama":"Hasnain A, Karl H. Learning Flow Scheduling. In: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE Computer Society. doi:https://doi.org/10.1109/CCNC49032.2021.9369514","apa":"Hasnain, A., & Karl, H. (n.d.). Learning Flow Scheduling. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). Las Vegas, USA: IEEE Computer Society. https://doi.org/10.1109/CCNC49032.2021.9369514"},"year":"2021","conference":{"end_date":"2021-01-12","location":"Las Vegas, USA","start_date":"2021-01-09","name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)"},"_id":"20125","publication":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","keyword":["Flow scheduling","Deadlines","Reinforcement learning"],"publisher":"IEEE Computer Society","author":[{"full_name":"Hasnain, Asif","first_name":"Asif","id":"63288","last_name":"Hasnain"},{"last_name":"Karl","id":"126","first_name":"Holger","full_name":"Karl, Holger"}],"date_created":"2020-10-19T14:27:17Z","status":"public","abstract":[{"lang":"eng","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."}],"user_id":"63288","ddc":["000"],"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:20Z","doi":"https://doi.org/10.1109/CCNC49032.2021.9369514","department":[{"_id":"75"}],"project":[{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C4","_id":"16"},{"name":"SFB 901","_id":"1"}],"publication_status":"accepted","title":"Learning Flow Scheduling"},{"publication_status":"accepted","project":[{"name":"SFB 901 - Subproject C4","_id":"16"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"1","name":"SFB 901"}],"department":[{"_id":"75"}],"title":"Learning Coflow Admissions","related_material":{"link":[{"url":"https://ieeexplore.ieee.org/document/9484599","relation":"confirmation"}]},"language":[{"iso":"eng"}],"doi":"10.1109/INFOCOMWKSHPS51825.2021.9484599","date_updated":"2022-01-06T06:54:42Z","status":"public","date_created":"2021-01-16T18:24:19Z","publisher":"IEEE Communications Society","author":[{"full_name":"Hasnain, Asif","first_name":"Asif","id":"63288","last_name":"Hasnain"},{"full_name":"Karl, Holger","first_name":"Holger","id":"126","last_name":"Karl"}],"keyword":["Coflow scheduling","Reinforcement learning","Deadlines"],"publication":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","ddc":["000"],"user_id":"63288","abstract":[{"lang":"eng","text":"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."}],"citation":{"bibtex":"@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={10.1109/INFOCOMWKSHPS51825.2021.9484599}, booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif and Karl, Holger} }","mla":"Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, doi:10.1109/INFOCOMWKSHPS51825.2021.9484599.","chicago":"Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE Communications Society, n.d. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599.","apa":"Hasnain, A., & Karl, H. (n.d.). Learning Coflow Admissions. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Vancouver BC Canada: IEEE Communications Society. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599","ama":"Hasnain A, Karl H. Learning Coflow Admissions. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE Communications Society. doi:10.1109/INFOCOMWKSHPS51825.2021.9484599","ieee":"A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver BC Canada.","short":"A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d."},"year":"2021","type":"conference","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9484599"}],"_id":"21005","conference":{"end_date":"2021-05-13","location":"Vancouver BC Canada","start_date":"2021-05-10","name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications"}},{"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:53:04Z","doi":"https://doi.org/10.1109/CCGrid49817.2020.00010","department":[{"_id":"75"}],"project":[{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - Subproject C4"},{"name":"SFB 901","_id":"1"}],"publication_status":"published","title":"Coflow Scheduling with Performance Guarantees for Data Center Applications","main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/9139642"}],"type":"conference","citation":{"ieee":"A. Hasnain and H. Karl, “Coflow Scheduling with Performance Guarantees for Data Center Applications,” in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), Melbourne, Australia, 2020.","short":"A. Hasnain, H. Karl, in: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020.","mla":"Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees for Data Center Applications.” 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020, doi:https://doi.org/10.1109/CCGrid49817.2020.00010.","bibtex":"@inproceedings{Hasnain_Karl_2020, title={Coflow Scheduling with Performance Guarantees for Data Center Applications}, DOI={https://doi.org/10.1109/CCGrid49817.2020.00010}, booktitle={2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger}, year={2020} }","apa":"Hasnain, A., & Karl, H. (2020). Coflow Scheduling with Performance Guarantees for Data Center Applications. In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). Melbourne, Australia: IEEE Computer Society. https://doi.org/10.1109/CCGrid49817.2020.00010","ama":"Hasnain A, Karl H. Coflow Scheduling with Performance Guarantees for Data Center Applications. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE Computer Society; 2020. doi:https://doi.org/10.1109/CCGrid49817.2020.00010","chicago":"Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees for Data Center Applications.” In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE Computer Society, 2020. https://doi.org/10.1109/CCGrid49817.2020.00010."},"year":"2020","_id":"17082","conference":{"name":"2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)","location":"Melbourne, Australia"},"author":[{"id":"63288","last_name":"Hasnain","full_name":"Hasnain, Asif","first_name":"Asif"},{"full_name":"Karl, Holger","first_name":"Holger","id":"126","last_name":"Karl"}],"publisher":"IEEE Computer Society","publication":"2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)","keyword":["Coflow","Scheduling","Deadlines","Data centers"],"status":"public","date_created":"2020-06-06T07:40:45Z","abstract":[{"lang":"eng","text":"Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines."}],"user_id":"63288","ddc":["000"]}]