---
_id: '19609'
abstract:
- lang: eng
  text: "Modern services comprise interconnected components,\r\ne.g., microservices
    in a service mesh, that can scale and\r\nrun on multiple nodes across the network
    on demand. To process\r\nincoming traffic, service components have to be instantiated
    and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands
    into account. This challenge is usually solved with\r\ncustom approaches designed
    by experts. While this typically\r\nworks well for the considered scenario, the
    models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin
    practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement
    learning approach that\r\nlearns how to best coordinate services and is geared
    towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable,
    possibly delayed monitoring information. Rather than\r\ndefining a complex model
    or an algorithm how to achieve an\r\nobjective, our model-free approach adapts
    to various objectives\r\nand traffic patterns. An agent is trained offline without
    expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto
    a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and
    overall network utility on real-world network\r\ntopologies and traffic traces.
    It also learns to optimize different\r\nobjectives, generalizes to scenarios with
    unseen, stochastic traffic\r\npatterns, and scales to large real-world networks."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Adnan
  full_name: Manzoor, Adnan
  last_name: Manzoor
- first_name: Haydar
  full_name: Qarawlus, Haydar
  last_name: Qarawlus
- first_name: Rafael
  full_name: Schellenberg, Rafael
  last_name: Schellenberg
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
- first_name: Ramin
  full_name: Khalili, Ramin
  last_name: Khalili
- first_name: Artur
  full_name: Hecker, Artur
  last_name: Hecker
citation:
  ama: 'Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service
    Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference
    on Network and Service Management (CNSM)</i>. IEEE; 2020.'
  apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili,
    R., &#38; Hecker, A. (2020). Self-Driving Network and Service Coordination Using
    Deep Reinforcement Learning. In <i>IEEE International Conference on Network and
    Service Management (CNSM)</i>. IEEE.
  bibtex: '@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020,
    title={Self-Driving Network and Service Coordination Using Deep Reinforcement
    Learning}, booktitle={IEEE International Conference on Network and Service Management
    (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan
    and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin
    and Hecker, Artur}, year={2020} }'
  chicago: Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg,
    Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service
    Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference
    on Network and Service Management (CNSM)</i>. IEEE, 2020.
  ieee: S. B. Schneider <i>et al.</i>, “Self-Driving Network and Service Coordination
    Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Network
    and Service Management (CNSM)</i>, 2020.
  mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination
    Using Deep Reinforcement Learning.” <i>IEEE International Conference on Network
    and Service Management (CNSM)</i>, IEEE, 2020.
  short: 'S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili,
    A. Hecker, in: IEEE International Conference on Network and Service Management
    (CNSM), IEEE, 2020.'
date_created: 2020-09-22T06:28:22Z
date_updated: 2022-01-06T06:54:08Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2020-09-22T06:29:16Z
  date_updated: 2020-09-22T06:36:00Z
  file_id: '19610'
  file_name: ris_with_copyright.pdf
  file_size: 642999
  relation: main_file
file_date_updated: 2020-09-22T06:36:00Z
has_accepted_license: '1'
keyword:
- self-driving networks
- self-learning
- network coordination
- service coordination
- reinforcement learning
- deep learning
- nfv
language:
- iso: eng
oa: '1'
project:
- _id: '1'
  name: SFB 901
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '16'
  name: SFB 901 - Subproject C4
publication: IEEE International Conference on Network and Service Management (CNSM)
publisher: IEEE
status: public
title: Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2020'
...