---
_id: '20125'
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.
author:
- first_name: Asif
  full_name: Hasnain, Asif
  id: '63288'
  last_name: Hasnain
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  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>'
  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>'
  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}
    }'
  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>.
  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.
  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>.
  short: 'A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &#38;
    Networking Conference (CCNC), IEEE Computer Society, n.d.'
conference:
  end_date: 2021-01-12
  location: Las Vegas, USA
  name: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
  start_date: 2021-01-09
date_created: 2020-10-19T14:27:17Z
date_updated: 2022-01-06T06:54:20Z
ddc:
- '000'
department:
- _id: '75'
doi: https://doi.org/10.1109/CCNC49032.2021.9369514
keyword:
- Flow scheduling
- Deadlines
- Reinforcement learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9369514
project:
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '16'
  name: SFB 901 - Subproject C4
- _id: '1'
  name: SFB 901
publication: 2021 IEEE 18th Annual Consumer Communications & Networking Conference
  (CCNC)
publication_status: accepted
publisher: IEEE Computer Society
status: public
title: Learning Flow Scheduling
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '21005'
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.
author:
- first_name: Asif
  full_name: Hasnain, Asif
  id: '63288'
  last_name: Hasnain
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Hasnain A, Karl H. Learning Coflow Admissions. In: <i>IEEE INFOCOM 2021 -
    IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>. IEEE
    Communications Society. doi:<a href="https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>'
  apa: 'Hasnain, A., &#38; Karl, H. (n.d.). Learning Coflow Admissions. In <i>IEEE
    INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>.
    Vancouver BC Canada: IEEE Communications Society. <a href="https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599">https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599</a>'
  bibtex: '@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={<a
    href="https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>},
    booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
    (INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif
    and Karl, Holger} }'
  chicago: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In <i>IEEE
    INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>.
    IEEE Communications Society, n.d. <a href="https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599">https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599</a>.
  ieee: A. Hasnain and H. Karl, “Learning Coflow Admissions,” in <i>IEEE INFOCOM 2021
    - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>, Vancouver
    BC Canada.
  mla: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” <i>IEEE INFOCOM
    2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>,
    IEEE Communications Society, doi:<a href="https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>.
  short: 'A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer
    Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.'
conference:
  end_date: 2021-05-13
  location: Vancouver BC Canada
  name: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
  start_date: 2021-05-10
date_created: 2021-01-16T18:24:19Z
date_updated: 2022-01-06T06:54:42Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/INFOCOMWKSHPS51825.2021.9484599
keyword:
- Coflow scheduling
- Reinforcement learning
- Deadlines
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9484599
project:
- _id: '16'
  name: SFB 901 - Subproject C4
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '1'
  name: SFB 901
publication: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
  (INFOCOM WKSHPS)
publication_status: accepted
publisher: IEEE Communications Society
related_material:
  link:
  - relation: confirmation
    url: https://ieeexplore.ieee.org/document/9484599
status: public
title: Learning Coflow Admissions
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '17082'
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.
author:
- first_name: Asif
  full_name: Hasnain, Asif
  id: '63288'
  last_name: Hasnain
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Hasnain A, Karl H. Coflow Scheduling with Performance Guarantees for Data
    Center Applications. In: <i>2020 20th IEEE/ACM International Symposium on Cluster,
    Cloud and Internet Computing (CCGRID)</i>. IEEE Computer Society; 2020. doi:<a
    href="https://doi.org/10.1109/CCGrid49817.2020.00010">https://doi.org/10.1109/CCGrid49817.2020.00010</a>'
  apa: 'Hasnain, A., &#38; Karl, H. (2020). Coflow Scheduling with Performance Guarantees
    for Data Center Applications. In <i>2020 20th IEEE/ACM International Symposium
    on Cluster, Cloud and Internet Computing (CCGRID)</i>. Melbourne, Australia: IEEE
    Computer Society. <a href="https://doi.org/10.1109/CCGrid49817.2020.00010">https://doi.org/10.1109/CCGrid49817.2020.00010</a>'
  bibtex: '@inproceedings{Hasnain_Karl_2020, title={Coflow Scheduling with Performance
    Guarantees for Data Center Applications}, DOI={<a href="https://doi.org/10.1109/CCGrid49817.2020.00010">https://doi.org/10.1109/CCGrid49817.2020.00010</a>},
    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} }'
  chicago: Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees
    for Data Center Applications.” In <i>2020 20th IEEE/ACM International Symposium
    on Cluster, Cloud and Internet Computing (CCGRID)</i>. IEEE Computer Society,
    2020. <a href="https://doi.org/10.1109/CCGrid49817.2020.00010">https://doi.org/10.1109/CCGrid49817.2020.00010</a>.
  ieee: A. Hasnain and H. Karl, “Coflow Scheduling with Performance Guarantees for
    Data Center Applications,” in <i>2020 20th IEEE/ACM International Symposium on
    Cluster, Cloud and Internet Computing (CCGRID)</i>, Melbourne, Australia, 2020.
  mla: Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees
    for Data Center Applications.” <i>2020 20th IEEE/ACM International Symposium on
    Cluster, Cloud and Internet Computing (CCGRID)</i>, IEEE Computer Society, 2020,
    doi:<a href="https://doi.org/10.1109/CCGrid49817.2020.00010">https://doi.org/10.1109/CCGrid49817.2020.00010</a>.
  short: 'A. Hasnain, H. Karl, in: 2020 20th IEEE/ACM International Symposium on Cluster,
    Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020.'
conference:
  location: Melbourne, Australia
  name: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet
    Computing (CCGRID)
date_created: 2020-06-06T07:40:45Z
date_updated: 2022-01-06T06:53:04Z
ddc:
- '000'
department:
- _id: '75'
doi: https://doi.org/10.1109/CCGrid49817.2020.00010
keyword:
- Coflow
- Scheduling
- Deadlines
- Data centers
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/abstract/document/9139642
project:
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '16'
  name: SFB 901 - Subproject C4
- _id: '1'
  name: SFB 901
publication: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet
  Computing (CCGRID)
publication_status: published
publisher: IEEE Computer Society
status: public
title: Coflow Scheduling with Performance Guarantees for Data Center Applications
type: conference
user_id: '63288'
year: '2020'
...