---
_id: '30236'
abstract:
- lang: eng
  text: "Recent reinforcement learning approaches for continuous control in wireless
    mobile networks have shown impressive\r\nresults. But due to the lack of open
    and compatible simulators, authors typically create their own simulation environments
    for training and evaluation. This is cumbersome and time-consuming for authors
    and limits reproducibility and comparability, ultimately impeding progress in
    the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform
    for training, evaluating, and comparing reinforcement learning and conventional
    approaches for continuous control in mobile wireless networks. mobile-env is lightweight
    and implements the common OpenAI Gym interface and additional wrappers, which
    allows connecting virtually any single-agent or multi-agent reinforcement learning
    framework to the environment. While mobile-env provides sensible default values
    and can be used out of the box, it also has many configuration options and is
    easy to extend. We therefore believe mobile-env to be a valuable platform for
    driving meaningful progress in autonomous coordination of\r\nwireless mobile networks."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Stefan
  full_name: Werner, Stefan
  last_name: Werner
- first_name: Ramin
  full_name: Khalili, Ramin
  last_name: Khalili
- first_name: Artur
  full_name: Hecker, Artur
  last_name: Hecker
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform
    for Reinforcement Learning in Wireless Mobile Networks. In: <i>IEEE/IFIP Network
    Operations and Management Symposium (NOMS)</i>. IEEE; 2022.'
  apa: 'Schneider, S. B., Werner, S., Khalili, R., Hecker, A., &#38; Karl, H. (2022).
    mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.
    <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP
    Network Operations and Management Symposium (NOMS), Budapest.'
  bibtex: '@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env:
    An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP
    Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider,
    Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl,
    Holger}, year={2022} }'
  chicago: 'Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker,
    and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless
    Mobile Networks.” In <i>IEEE/IFIP Network Operations and Management Symposium
    (NOMS)</i>. IEEE, 2022.'
  ieee: 'S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env:
    An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented
    at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest,
    2022.'
  mla: 'Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement
    Learning in Wireless Mobile Networks.” <i>IEEE/IFIP Network Operations and Management
    Symposium (NOMS)</i>, IEEE, 2022.'
  short: 'S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP
    Network Operations and Management Symposium (NOMS), IEEE, 2022.'
conference:
  end_date: 2022-04-29
  location: Budapest
  name: IEEE/IFIP Network Operations and Management Symposium (NOMS)
  start_date: 2022-04-25
date_created: 2022-03-10T18:28:14Z
date_updated: 2022-03-10T18:28:19Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2022-03-10T18:25:41Z
  date_updated: 2022-03-10T18:25:41Z
  file_id: '30237'
  file_name: author_version.pdf
  file_size: 223412
  relation: main_file
file_date_updated: 2022-03-10T18:25:41Z
has_accepted_license: '1'
keyword:
- wireless mobile networks
- network management
- continuous control
- cognitive networks
- autonomous coordination
- reinforcement learning
- gym environment
- simulation
- open source
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'
publication: IEEE/IFIP Network Operations and Management Symposium (NOMS)
publisher: IEEE
quality_controlled: '1'
status: public
title: 'mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile
  Networks'
type: conference
user_id: '35343'
year: '2022'
...
---
_id: '29220'
abstract:
- lang: eng
  text: "Modern services often comprise several components, such as chained virtual
    network functions, microservices, or\r\nmachine learning functions. Providing
    such services requires to decide how often to instantiate each component, where
    to place these instances in the network, how to chain them and route traffic through
    them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep
    reinforcement learning (DRL) approaches for self-learning network and service
    management have emerged recently. These model-free DRL approaches are more flexible
    but typically learn tabula rasa, i.e., disregard existing understanding of networks,
    services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel
    model-based AI approach that leverages existing understanding of networks and
    services for more efficient and effective coordination without time-intensive
    training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic
    model. This allows FutureCoord to estimate the impact of future incoming traffic
    and effectively optimize long-term effects, taking fluctuating demand and Quality
    of Service (QoS) requirements into account. Our extensive evaluation based on
    real-world network topologies, services, and traffic traces indicates that FutureCoord
    clearly outperforms state-of-the-art model-free and model-based approaches with
    up to 51% higher flow success ratios."
author:
- first_name: Stefan
  full_name: Werner, Stefan
  last_name: Werner
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination
    Beyond Certainty. In: <i>IEEE/IFIP Network Operations and Management Symposium
    (NOMS)</i>. IEEE; 2022.'
  apa: 'Werner, S., Schneider, S. B., &#38; Karl, H. (2022). Use What You Know: Network
    and Service Coordination Beyond Certainty. <i>IEEE/IFIP Network Operations and
    Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium
    (NOMS), Budapest.'
  bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network
    and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations
    and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and
    Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }'
  chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What
    You Know: Network and Service Coordination Beyond Certainty.” In <i>IEEE/IFIP
    Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.'
  ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and
    Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations
    and Management Symposium (NOMS), Budapest, 2022.'
  mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination
    Beyond Certainty.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>,
    IEEE, 2022.'
  short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and
    Management Symposium (NOMS), IEEE, 2022.'
conference:
  end_date: 2022-04-29
  location: Budapest
  name: IEEE/IFIP Network Operations and Management Symposium (NOMS)
  start_date: 2022-04-25
date_created: 2022-01-11T08:43:26Z
date_updated: 2022-01-11T08:44:04Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2022-01-11T08:39:57Z
  date_updated: 2022-01-11T08:39:57Z
  file_id: '29222'
  file_name: author_version.pdf
  file_size: 528653
  relation: main_file
file_date_updated: 2022-01-11T08:39:57Z
has_accepted_license: '1'
keyword:
- network management
- service management
- AI
- Monte Carlo Tree Search
- model-based
- QoS
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'
publication: IEEE/IFIP Network Operations and Management Symposium (NOMS)
publisher: IEEE
quality_controlled: '1'
status: public
title: 'Use What You Know: Network and Service Coordination Beyond Certainty'
type: conference
user_id: '35343'
year: '2022'
...
---
_id: '21543'
abstract:
- lang: eng
  text: "Services often consist of multiple chained components such as microservices
    in a service mesh, or machine learning functions in a pipeline. Providing these
    services requires online coordination including scaling the service, placing instance
    of all components in the network, scheduling traffic to these instances, and routing
    traffic through the network. Optimized service coordination is still a hard problem
    due to many influencing factors such as rapidly arriving user demands and limited
    node and link capacity. Existing approaches to solve the problem are often built
    on rigid models and assumptions, tailored to specific scenarios. If the scenario
    changes and the assumptions no longer hold, they easily break and require manual
    adjustments by experts. Novel self-learning approaches using deep reinforcement
    learning (DRL) are promising but still have limitations as they only address simplified
    versions of the problem and are typically centralized and thus do not scale to
    practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed
    self-learning service coordination approach using DRL. After centralized training,
    we deploy a distributed DRL agent at each node in the network, making fast coordination
    decisions locally in parallel with the other nodes. Each agent only observes its
    direct neighbors and does not need global knowledge. Hence, our approach scales
    independently from the size of the network. In our extensive evaluation using
    real-world network topologies and traffic traces, we show that our proposed approach
    outperforms a state-of-the-art conventional heuristic as well as a centralized
    DRL approach (60% higher throughput on average) while requiring less time per
    online decision (1 ms)."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Haydar
  full_name: Qarawlus, Haydar
  last_name: Qarawlus
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination
    Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Distributed
    Computing Systems (ICDCS)</i>. IEEE; 2021.'
  apa: 'Schneider, S. B., Qarawlus, H., &#38; Karl, H. (2021). Distributed Online
    Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International
    Conference on Distributed Computing Systems (ICDCS)</i>. Washington, DC, USA:
    IEEE.'
  bibtex: '@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online
    Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International
    Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider,
    Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }'
  chicago: Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed
    Online Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International
    Conference on Distributed Computing Systems (ICDCS)</i>. IEEE, 2021.
  ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination
    Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Distributed
    Computing Systems (ICDCS)</i>, Washington, DC, USA, 2021.
  mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination
    Using Deep Reinforcement Learning.” <i>IEEE International Conference on Distributed
    Computing Systems (ICDCS)</i>, IEEE, 2021.
  short: 'S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference
    on Distributed Computing Systems (ICDCS), IEEE, 2021.'
conference:
  location: Washington, DC, USA
  name: IEEE International Conference on Distributed Computing Systems (ICDCS)
date_created: 2021-03-18T17:15:47Z
date_updated: 2022-01-06T06:55:04Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2021-03-18T17:12:56Z
  date_updated: 2021-03-18T17:12:56Z
  file_id: '21544'
  file_name: public_author_version.pdf
  file_size: 606321
  relation: main_file
  title: Distributed Online Service Coordination Using Deep Reinforcement Learning
file_date_updated: 2021-03-18T17:12:56Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- reinforcement learning
- distributed
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 Distributed Computing Systems (ICDCS)
publisher: IEEE
related_material:
  link:
  - relation: software
    url: https://github.com/ RealVNF/distributed-drl-coordination
status: public
title: Distributed Online Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2021'
...
---
_id: '20693'
abstract:
- lang: eng
  text: "In practical, large-scale networks, services are requested\r\nby users across
    the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected
    components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires
    scaling them according to continuously changing user\r\ndemand, deploying instances
    at the edge close to their users,\r\nand routing traffic efficiently between users
    and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough
    centralized approaches, where a single coordinator\r\nknows everything and coordinates
    the entire network globally.\r\nWhile such centralized approaches can reach global
    optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches
    scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge
    and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination
    approach\r\nthat combines the good solution quality of centralized approaches\r\nwith
    the scalability of distributed approaches. In doing so, we divide\r\nthe network
    into multiple hierarchical domains and optimize\r\ncoordination in a top-down
    manner. We compare our hierarchical\r\nwith a centralized approach in an extensive
    evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination
    can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized
    approaches."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Mirko
  full_name: Jürgens, Mirko
  last_name: Jürgens
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
citation:
  ama: 'Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network
    and Service Coordination. In: <i>IFIP/IEEE International Symposium on Integrated
    Network Management (IM)</i>. IFIP/IEEE; 2021.'
  apa: 'Schneider, S. B., Jürgens, M., &#38; Karl, H. (2021). Divide and Conquer:
    Hierarchical Network and Service Coordination. In <i>IFIP/IEEE International Symposium
    on Integrated Network Management (IM)</i>. Bordeaux, France: IFIP/IEEE.'
  bibtex: '@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer:
    Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International
    Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider,
    Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }'
  chicago: 'Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and
    Conquer: Hierarchical Network and Service Coordination.” In <i>IFIP/IEEE International
    Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE, 2021.'
  ieee: 'S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical
    Network and Service Coordination,” in <i>IFIP/IEEE International Symposium on
    Integrated Network Management (IM)</i>, Bordeaux, France, 2021.'
  mla: 'Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network
    and Service Coordination.” <i>IFIP/IEEE International Symposium on Integrated
    Network Management (IM)</i>, IFIP/IEEE, 2021.'
  short: 'S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium
    on Integrated Network Management (IM), IFIP/IEEE, 2021.'
conference:
  location: Bordeaux, France
  name: IFIP/IEEE International Symposium on Integrated Network Management (IM)
date_created: 2020-12-11T08:39:47Z
date_updated: 2022-01-06T06:54:32Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2020-12-11T08:37:37Z
  date_updated: 2020-12-11T08:37:37Z
  file_id: '20694'
  file_name: preprint_with_header.pdf
  file_size: 7979772
  relation: main_file
  title: 'Divide and Conquer: Hierarchical Network and Service Coordination'
file_date_updated: 2020-12-11T08:37:37Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- hierarchical
- scalability
- 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: IFIP/IEEE International Symposium on Integrated Network Management (IM)
publisher: IFIP/IEEE
quality_controlled: '1'
status: public
title: 'Divide and Conquer: Hierarchical Network and Service Coordination'
type: conference
user_id: '35343'
year: '2021'
...
---
_id: '21808'
abstract:
- lang: eng
  text: "Modern services consist of interconnected components,e.g., microservices
    in a service mesh or machine learning functions in a pipeline. These services
    can scale and run across multiple network nodes on demand. To process incoming
    traffic, service components have to be instantiated and traffic assigned to these
    instances, taking capacities, changing demands, and Quality of Service (QoS) requirements
    into account. This challenge is usually solved with custom approaches designed
    by experts. While this typically works well for the considered scenario, the models
    often rely on unrealistic assumptions or on knowledge that is not available in
    practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep
    reinforcement learning approach that learns how to best coordinate services and
    is geared towards realistic assumptions. It interacts with the network and relies
    on available, possibly delayed monitoring information. Rather than defining a
    complex model or an algorithm on how to achieve an objective, our model-free approach
    adapts to various objectives and traffic patterns. An agent is trained offline
    without expert knowledge and then applied online with minimal overhead. Compared
    to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput
    (up to 76%) and overall network utility (more than 2x) on realworld network topologies
    and traffic traces. It also supports optimizing multiple, possibly competing objectives,
    learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic
    traffic, and scales to large real-world networks. For reproducibility and reuse,
    our code is publicly available."
article_type: original
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Ramin
  full_name: Khalili, Ramin
  last_name: Khalili
- 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: Artur
  full_name: Hecker, Artur
  last_name: Hecker
citation:
  ama: Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service
    Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and
    Service Management</i>. 2021. doi:<a href="https://doi.org/10.1109/TNSM.2021.3076503">10.1109/TNSM.2021.3076503</a>
  apa: Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R.,
    Karl, H., &#38; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination
    Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>.
    <a href="https://doi.org/10.1109/TNSM.2021.3076503">https://doi.org/10.1109/TNSM.2021.3076503</a>
  bibtex: '@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021,
    title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement
    Learning}, DOI={<a href="https://doi.org/10.1109/TNSM.2021.3076503">10.1109/TNSM.2021.3076503</a>},
    journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider,
    Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and
    Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }'
  chicago: Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus,
    Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective
    Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network
    and Service Management</i>, 2021. <a href="https://doi.org/10.1109/TNSM.2021.3076503">https://doi.org/10.1109/TNSM.2021.3076503</a>.
  ieee: S. B. Schneider <i>et al.</i>, “Self-Learning Multi-Objective Service Coordination
    Using Deep Reinforcement Learning,” <i>Transactions on Network and Service Management</i>,
    2021.
  mla: Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service
    Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and
    Service Management</i>, IEEE, 2021, doi:<a href="https://doi.org/10.1109/TNSM.2021.3076503">10.1109/TNSM.2021.3076503</a>.
  short: S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H.
    Karl, A. Hecker, Transactions on Network and Service Management (2021).
date_created: 2021-04-27T08:04:16Z
date_updated: 2022-01-06T06:55:15Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/TNSM.2021.3076503
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2021-04-27T08:01:26Z
  date_updated: 2021-04-27T08:01:26Z
  description: Author version of the accepted paper
  file_id: '21809'
  file_name: ris-accepted-version.pdf
  file_size: 4172270
  relation: main_file
file_date_updated: 2021-04-27T08:01:26Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- reinforcement learning
- self-learning
- self-adaptation
- multi-objective
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: Transactions on Network and Service Management
publisher: IEEE
status: public
title: Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement
  Learning
type: journal_article
user_id: '35343'
year: '2021'
...