---
_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: '17663'
abstract:
- lang: eng
  text: 'In this paper, we define and study a new problem, referred to as the Dependent
    Unsplittable Flow Problem (D-UFP). We present and discuss this problem in the
    context of large-scale powerful (radar/camera) sensor networks, but we believe
    it has important applications on the admission of large flows in other networks
    as well. In order to optimize the selection of flows transmitted to the gateway,
    D-UFP takes into account possible dependencies between flows. We show that D-UFP
    is more difficult than NP-hard problems for which no good approximation is known.
    Then, we address two special cases of this problem: the case where all the sensors
    have a shared channel and the case where the sensors form a mesh and route to
    the gateway over a spanning tree.'
author:
- first_name: R.
  full_name: Cohen, R.
  last_name: Cohen
- first_name: I.
  full_name: Nudelman, I.
  last_name: Nudelman
- first_name: Gleb
  full_name: Polevoy, Gleb
  id: '83983'
  last_name: Polevoy
citation:
  ama: Cohen R, Nudelman I, Polevoy G. On the Admission of Dependent Flows in Powerful
    Sensor Networks. <i>Networking, IEEE/ACM Transactions on</i>. 2013;21(5):1461-1471.
    doi:<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>
  apa: Cohen, R., Nudelman, I., &#38; Polevoy, G. (2013). On the Admission of Dependent
    Flows in Powerful Sensor Networks. <i>Networking, IEEE/ACM Transactions On</i>,
    <i>21</i>(5), 1461–1471. <a href="https://doi.org/10.1109/TNET.2012.2227792">https://doi.org/10.1109/TNET.2012.2227792</a>
  bibtex: '@article{Cohen_Nudelman_Polevoy_2013, title={On the Admission of Dependent
    Flows in Powerful Sensor Networks}, volume={21}, DOI={<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>},
    number={5}, journal={Networking, IEEE/ACM Transactions on}, author={Cohen, R.
    and Nudelman, I. and Polevoy, Gleb}, year={2013}, pages={1461–1471} }'
  chicago: 'Cohen, R., I. Nudelman, and Gleb Polevoy. “On the Admission of Dependent
    Flows in Powerful Sensor Networks.” <i>Networking, IEEE/ACM Transactions On</i>
    21, no. 5 (2013): 1461–71. <a href="https://doi.org/10.1109/TNET.2012.2227792">https://doi.org/10.1109/TNET.2012.2227792</a>.'
  ieee: R. Cohen, I. Nudelman, and G. Polevoy, “On the Admission of Dependent Flows
    in Powerful Sensor Networks,” <i>Networking, IEEE/ACM Transactions on</i>, vol.
    21, no. 5, pp. 1461–1471, 2013.
  mla: Cohen, R., et al. “On the Admission of Dependent Flows in Powerful Sensor Networks.”
    <i>Networking, IEEE/ACM Transactions On</i>, vol. 21, no. 5, 2013, pp. 1461–71,
    doi:<a href="https://doi.org/10.1109/TNET.2012.2227792">10.1109/TNET.2012.2227792</a>.
  short: R. Cohen, I. Nudelman, G. Polevoy, Networking, IEEE/ACM Transactions On 21
    (2013) 1461–1471.
date_created: 2020-08-06T15:22:05Z
date_updated: 2022-01-06T06:53:16Z
department:
- _id: '63'
- _id: '541'
doi: 10.1109/TNET.2012.2227792
extern: '1'
intvolume: '        21'
issue: '5'
keyword:
- Approximation algorithms
- Approximation methods
- Bandwidth
- Logic gates
- Radar
- Vectors
- Wireless sensor networks
- Dependent flow scheduling
- sensor networks
language:
- iso: eng
page: 1461-1471
publication: Networking, IEEE/ACM Transactions on
publication_identifier:
  issn:
  - 1063-6692
status: public
title: On the Admission of Dependent Flows in Powerful Sensor Networks
type: journal_article
user_id: '83983'
volume: 21
year: '2013'
...