---
_id: '35889'
abstract:
- lang: eng
  text: Network and service coordination is important to provide modern services consisting
    of multiple interconnected components, e.g., in 5G, network function virtualization
    (NFV), or cloud and edge computing. In this paper, I outline my dissertation research,
    which proposes six approaches to automate such network and service coordination.
    All approaches dynamically react to the current demand and optimize coordination
    for high service quality and low costs. The approaches range from centralized
    to distributed methods and from conventional heuristic algorithms and mixed-integer
    linear programs to machine learning approaches using supervised and reinforcement
    learning. I briefly discuss their main ideas and advantages over other state-of-the-art
    approaches and compare strengths and weaknesses.
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
citation:
  ama: Schneider SB. <i>Conventional and Machine Learning Approaches for Network and
    Service Coordination</i>.; 2021.
  apa: Schneider, S. B. (2021). <i>Conventional and Machine Learning Approaches for
    Network and Service Coordination</i>.
  bibtex: '@book{Schneider_2021, title={Conventional and Machine Learning Approaches
    for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021}
    }'
  chicago: Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches
    for Network and Service Coordination</i>, 2021.
  ieee: S. B. Schneider, <i>Conventional and Machine Learning Approaches for Network
    and Service Coordination</i>. 2021.
  mla: Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches
    for Network and Service Coordination</i>. 2021.
  short: S.B. Schneider, Conventional and Machine Learning Approaches for Network
    and Service Coordination, 2021.
date_created: 2023-01-10T15:08:50Z
date_updated: 2023-01-10T15:09:05Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2023-01-10T15:07:03Z
  date_updated: 2023-01-10T15:07:03Z
  file_id: '35890'
  file_name: main.pdf
  file_size: 133340
  relation: main_file
file_date_updated: 2023-01-10T15:07:03Z
has_accepted_license: '1'
keyword:
- nfv
- coordination
- machine learning
- reinforcement learning
- phd
- digest
language:
- iso: eng
oa: '1'
project:
- _id: '1'
  name: 'SFB 901: SFB 901'
- _id: '4'
  name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
  name: 'SFB 901 - C4: SFB 901 - Subproject C4'
status: public
title: Conventional and Machine Learning Approaches for Network and Service Coordination
type: working_paper
user_id: '35343'
year: '2021'
...