---
_id: '65309'
author:
- first_name: Simon
full_name: Hemmrich, Simon
id: '83557'
last_name: Hemmrich
citation:
ama: 'Hemmrich S. A Design Theory for Blockchain-Based Reputation Systems : Trust
and Coordination in B2B Markets. Universität Paderborn; 2025. doi:https://doi.org/10.17619/UNIPB/1-2414'
apa: 'Hemmrich, S. (2025). A Design Theory for Blockchain-Based Reputation Systems :
Trust and Coordination in B2B Markets. Universität Paderborn. https://doi.org/10.17619/UNIPB/1-2414'
bibtex: '@book{Hemmrich_2025, place={Paderborn}, title={A Design Theory for Blockchain-Based
Reputation Systems : Trust and Coordination in B2B Markets}, DOI={https://doi.org/10.17619/UNIPB/1-2414},
publisher={Universität Paderborn}, author={Hemmrich, Simon}, year={2025} }'
chicago: 'Hemmrich, Simon. A Design Theory for Blockchain-Based Reputation Systems :
Trust and Coordination in B2B Markets. Paderborn: Universität Paderborn, 2025.
https://doi.org/10.17619/UNIPB/1-2414.'
ieee: 'S. Hemmrich, A Design Theory for Blockchain-Based Reputation Systems :
Trust and Coordination in B2B Markets. Paderborn: Universität Paderborn, 2025.'
mla: 'Hemmrich, Simon. A Design Theory for Blockchain-Based Reputation Systems :
Trust and Coordination in B2B Markets. Universität Paderborn, 2025, doi:https://doi.org/10.17619/UNIPB/1-2414.'
short: 'S. Hemmrich, A Design Theory for Blockchain-Based Reputation Systems : Trust
and Coordination in B2B Markets, Universität Paderborn, Paderborn, 2025.'
date_created: 2026-04-02T03:52:09Z
date_updated: 2026-04-02T04:31:57Z
department:
- _id: '195'
doi: https://doi.org/10.17619/UNIPB/1-2414
jel:
- D8
keyword:
- Reputation Systems
- Rating systems
- monetary ratings
- incentive mechanism
- systems theory
- Market coordination
- advanced review system
language:
- iso: eng
page: '347'
place: Paderborn
publication_status: published
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Daniel
full_name: Beverungen, Daniel
last_name: Beverungen
- first_name: Dennis
full_name: Kundisch, Dennis
last_name: Kundisch
title: 'A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination
in B2B Markets'
type: dissertation
user_id: '83557'
year: '2025'
...
---
_id: '64002'
abstract:
- lang: eng
text: The production of formaldehyde on industrial scale requires huge amounts of
energy due to the involvement of reforming processes in combination with the demand
in the megaton scale. Hence, a direct route for the transformation of (bio)methane
to formaldehyde would decrease costs and puts less pressure on the environment.
Herein, we report on the use of zinc modified silicas as possible support materials
for vanadium catalysts and the resulting consequences for the performance in the
selective oxidation of methane to formaldehyde. After optimization of the Zn content
and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1
was achieved. As a result of the extensive characterization by means of UV–vis,
Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity
of highly dispersed zinc atoms which promote the formation of active vanadium
species as supposed by theoretical calculations. This work presents a further
step of catalyst development towards direct industrial methane conversion which
may help to overcome current limitations in the future.
author:
- first_name: Benny
full_name: Kunkel, Benny
last_name: Kunkel
- first_name: Dominik
full_name: Seeburg, Dominik
last_name: Seeburg
- first_name: Anke
full_name: Kabelitz, Anke
last_name: Kabelitz
- first_name: Steffen
full_name: Witte, Steffen
last_name: Witte
- first_name: Torsten
full_name: Gutmann, Torsten
id: '118165'
last_name: Gutmann
- first_name: Hergen
full_name: Breitzke, Hergen
last_name: Breitzke
- first_name: Gerd
full_name: Buntkowsky, Gerd
last_name: Buntkowsky
- first_name: Ana Guilherme
full_name: Buzanich, Ana Guilherme
last_name: Buzanich
- first_name: Sebastian
full_name: Wohlrab, Sebastian
last_name: Wohlrab
citation:
ama: Kunkel B, Seeburg D, Kabelitz A, et al. Highly productive V/Zn-SiO2 catalysts
for the selective oxidation of methane. Catalysis Today. 2024;432:114643.
doi:10.1016/j.cattod.2024.114643
apa: Kunkel, B., Seeburg, D., Kabelitz, A., Witte, S., Gutmann, T., Breitzke, H.,
Buntkowsky, G., Buzanich, A. G., & Wohlrab, S. (2024). Highly productive V/Zn-SiO2
catalysts for the selective oxidation of methane. Catalysis Today, 432,
114643. https://doi.org/10.1016/j.cattod.2024.114643
bibtex: '@article{Kunkel_Seeburg_Kabelitz_Witte_Gutmann_Breitzke_Buntkowsky_Buzanich_Wohlrab_2024,
title={Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane},
volume={432}, DOI={10.1016/j.cattod.2024.114643},
journal={Catalysis Today}, author={Kunkel, Benny and Seeburg, Dominik and Kabelitz,
Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky,
Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}, year={2024}, pages={114643}
}'
chicago: 'Kunkel, Benny, Dominik Seeburg, Anke Kabelitz, Steffen Witte, Torsten
Gutmann, Hergen Breitzke, Gerd Buntkowsky, Ana Guilherme Buzanich, and Sebastian
Wohlrab. “Highly Productive V/Zn-SiO2 Catalysts for the Selective Oxidation of
Methane.” Catalysis Today 432 (2024): 114643. https://doi.org/10.1016/j.cattod.2024.114643.'
ieee: 'B. Kunkel et al., “Highly productive V/Zn-SiO2 catalysts for the selective
oxidation of methane,” Catalysis Today, vol. 432, p. 114643, 2024, doi:
10.1016/j.cattod.2024.114643.'
mla: Kunkel, Benny, et al. “Highly Productive V/Zn-SiO2 Catalysts for the Selective
Oxidation of Methane.” Catalysis Today, vol. 432, 2024, p. 114643, doi:10.1016/j.cattod.2024.114643.
short: B. Kunkel, D. Seeburg, A. Kabelitz, S. Witte, T. Gutmann, H. Breitzke, G.
Buntkowsky, A.G. Buzanich, S. Wohlrab, Catalysis Today 432 (2024) 114643.
date_created: 2026-02-07T15:53:56Z
date_updated: 2026-02-17T16:15:41Z
doi: 10.1016/j.cattod.2024.114643
extern: '1'
intvolume: ' 432'
keyword:
- Formaldehyde
- Local coordination
- SBA-15
- Vanadium oxo species
- XANES
- Zinc doped silica
language:
- iso: eng
page: '114643'
publication: Catalysis Today
status: public
title: Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane
type: journal_article
user_id: '100715'
volume: 432
year: '2024'
...
---
_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: IEEE/IFIP Network
Operations and Management Symposium (NOMS). IEEE; 2022.'
apa: 'Schneider, S. B., Werner, S., Khalili, R., Hecker, A., & Karl, H. (2022).
mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.
IEEE/IFIP Network Operations and Management Symposium (NOMS). 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 IEEE/IFIP Network Operations and Management Symposium
(NOMS). 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.” IEEE/IFIP Network Operations and Management
Symposium (NOMS), 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: '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: IEEE International Conference on Distributed
Computing Systems (ICDCS). IEEE; 2021.'
apa: 'Schneider, S. B., Qarawlus, H., & Karl, H. (2021). Distributed Online
Service Coordination Using Deep Reinforcement Learning. In IEEE International
Conference on Distributed Computing Systems (ICDCS). 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 IEEE International
Conference on Distributed Computing Systems (ICDCS). IEEE, 2021.
ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed
Computing Systems (ICDCS), Washington, DC, USA, 2021.
mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Distributed
Computing Systems (ICDCS), 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: IFIP/IEEE International Symposium on Integrated
Network Management (IM). IFIP/IEEE; 2021.'
apa: 'Schneider, S. B., Jürgens, M., & Karl, H. (2021). Divide and Conquer:
Hierarchical Network and Service Coordination. In IFIP/IEEE International Symposium
on Integrated Network Management (IM). 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 IFIP/IEEE International
Symposium on Integrated Network Management (IM). IFIP/IEEE, 2021.'
ieee: 'S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical
Network and Service Coordination,” in IFIP/IEEE International Symposium on
Integrated Network Management (IM), Bordeaux, France, 2021.'
mla: 'Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network
and Service Coordination.” IFIP/IEEE International Symposium on Integrated
Network Management (IM), 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. Transactions on Network and
Service Management. 2021. doi:10.1109/TNSM.2021.3076503
apa: Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R.,
Karl, H., & Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination
Using Deep Reinforcement Learning. Transactions on Network and Service Management.
https://doi.org/10.1109/TNSM.2021.3076503
bibtex: '@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021,
title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement
Learning}, DOI={10.1109/TNSM.2021.3076503},
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.” Transactions on Network
and Service Management, 2021. https://doi.org/10.1109/TNSM.2021.3076503.
ieee: S. B. Schneider et al., “Self-Learning Multi-Objective Service Coordination
Using Deep Reinforcement Learning,” Transactions on Network and Service Management,
2021.
mla: Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service
Coordination Using Deep Reinforcement Learning.” Transactions on Network and
Service Management, IEEE, 2021, doi:10.1109/TNSM.2021.3076503.
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'
...
---
_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. Conventional and Machine Learning Approaches for Network and
Service Coordination.; 2021.
apa: Schneider, S. B. (2021). Conventional and Machine Learning Approaches for
Network and Service Coordination.
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. Conventional and Machine Learning Approaches
for Network and Service Coordination, 2021.
ieee: S. B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination. 2021.
mla: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination. 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'
...
---
_id: '19607'
abstract:
- lang: eng
text: "Modern services consist of modular, interconnected\r\ncomponents, e.g., microservices
forming a service mesh. To\r\ndynamically adjust to ever-changing service demands,
service\r\ncomponents have to be instantiated on nodes across the network.\r\nIncoming
flows requesting a service then need to be routed\r\nthrough the deployed instances
while considering node and link\r\ncapacities. Ultimately, the goal is to maximize
the successfully\r\nserved flows and Quality of Service (QoS) through online service\r\ncoordination.
Current approaches for service coordination are\r\nusually centralized, assuming
up-to-date global knowledge and\r\nmaking global decisions for all nodes in the
network. Such global\r\nknowledge and centralized decisions are not realistic
in practical\r\nlarge-scale networks.\r\n\r\nTo solve this problem, we propose
two algorithms for fully\r\ndistributed service coordination. The proposed algorithms
can be\r\nexecuted individually at each node in parallel and require only\r\nvery
limited global knowledge. We compare and evaluate both\r\nalgorithms with a state-of-the-art
centralized approach in extensive\r\nsimulations on a large-scale, real-world
network topology.\r\nOur results indicate that the two algorithms can compete
with\r\ncentralized approaches in terms of solution quality but require\r\nless
global knowledge and are magnitudes faster (more than\r\n100x)."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Lars Dietrich
full_name: Klenner, Lars Dietrich
last_name: Klenner
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Klenner LD, Karl H. Every Node for Itself: Fully Distributed
Service Coordination. In: IEEE International Conference on Network and Service
Management (CNSM). IEEE; 2020.'
apa: 'Schneider, S. B., Klenner, L. D., & Karl, H. (2020). Every Node for Itself:
Fully Distributed Service Coordination. In IEEE International Conference on
Network and Service Management (CNSM). IEEE.'
bibtex: '@inproceedings{Schneider_Klenner_Karl_2020, title={Every Node for Itself:
Fully Distributed Service Coordination}, booktitle={IEEE International Conference
on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider,
Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}, year={2020} }'
chicago: 'Schneider, Stefan Balthasar, Lars Dietrich Klenner, and Holger Karl. “Every
Node for Itself: Fully Distributed Service Coordination.” In IEEE International
Conference on Network and Service Management (CNSM). IEEE, 2020.'
ieee: 'S. B. Schneider, L. D. Klenner, and H. Karl, “Every Node for Itself: Fully
Distributed Service Coordination,” in IEEE International Conference on Network
and Service Management (CNSM), 2020.'
mla: 'Schneider, Stefan Balthasar, et al. “Every Node for Itself: Fully Distributed
Service Coordination.” IEEE International Conference on Network and Service
Management (CNSM), IEEE, 2020.'
short: 'S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference
on Network and Service Management (CNSM), IEEE, 2020.'
date_created: 2020-09-22T06:23:40Z
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: 2020-09-22T06:25:57Z
date_updated: 2020-09-22T06:36:25Z
file_id: '19608'
file_name: ris_with_copyright.pdf
file_size: 500948
relation: main_file
file_date_updated: 2020-09-22T06:36:25Z
has_accepted_license: '1'
keyword:
- distributed management
- service coordination
- network coordination
- nfv
- softwarization
- orchestration
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: 'Every Node for Itself: Fully Distributed Service Coordination'
type: conference
user_id: '35343'
year: '2020'
...
---
_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: IEEE International Conference
on Network and Service Management (CNSM). IEEE; 2020.'
apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili,
R., & Hecker, A. (2020). Self-Driving Network and Service Coordination Using
Deep Reinforcement Learning. In IEEE International Conference on Network and
Service Management (CNSM). 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 IEEE International Conference
on Network and Service Management (CNSM). IEEE, 2020.
ieee: S. B. Schneider et al., “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Network
and Service Management (CNSM), 2020.
mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Network
and Service Management (CNSM), 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'
...
---
_id: '5675'
abstract:
- lang: eng
text: When responding to natural disasters, professional relief units are often
supported by many volunteers which are not affiliated to humanitarian organizations.
The effective coordination of these volunteers is crucial to leverage their capabilities
and to avoid conflicts with professional relief units. In this paper, we empirically
identify key requirements that professional relief units pose on this coordination.
Based on these requirements, we suggest a decision model. We computationally solve
a real-world instance of the model and empirically validate the computed solution
in interviews with practitioners. Our results show that the suggested model allows
for solving volunteer coordination tasks of realistic size near-optimally within
short time, with the determined solution being well accepted by practitioners.
We also describe in this article how the suggested decision support model is integrated
in the volunteer coordination system which we develop in joint cooperation with
a disaster management authority and a software development company.
author:
- first_name: Gerhard
full_name: Rauchecker, Gerhard
last_name: Rauchecker
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Rauchecker G, Schryen G. Decision Support for the Optimal Coordination of
Spontaneous Volunteers in Disaster Relief. In: Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management. ; 2018.'
apa: Rauchecker, G., & Schryen, G. (2018). Decision Support for the Optimal
Coordination of Spontaneous Volunteers in Disaster Relief. In Proceedings of
the 15th International Conference on Information Systems for Crisis Response and
Management. Rochester, NY, USA.
bibtex: '@inproceedings{Rauchecker_Schryen_2018, title={Decision Support for the
Optimal Coordination of Spontaneous Volunteers in Disaster Relief}, booktitle={Proceedings
of the 15th International Conference on Information Systems for Crisis Response
and Management}, author={Rauchecker, Gerhard and Schryen, Guido}, year={2018}
}'
chicago: Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal
Coordination of Spontaneous Volunteers in Disaster Relief.” In Proceedings
of the 15th International Conference on Information Systems for Crisis Response
and Management, 2018.
ieee: G. Rauchecker and G. Schryen, “Decision Support for the Optimal Coordination
of Spontaneous Volunteers in Disaster Relief,” in Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management, Rochester,
NY, USA, 2018.
mla: Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal Coordination
of Spontaneous Volunteers in Disaster Relief.” Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management, 2018.
short: 'G. Rauchecker, G. Schryen, in: Proceedings of the 15th International Conference
on Information Systems for Crisis Response and Management, 2018.'
conference:
location: Rochester, NY, USA
name: 15th International Conference on Information Systems for Crisis Response and
Management
date_created: 2018-11-14T15:35:54Z
date_updated: 2022-01-06T07:02:28Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-07T11:25:06Z
date_updated: 2018-12-13T15:05:44Z
file_id: '6020'
file_name: 2018_ISCRAM_Conference_Proceedings - Publication Version.pdf
file_size: 488472
relation: main_file
file_date_updated: 2018-12-13T15:05:44Z
has_accepted_license: '1'
keyword:
- Coordination of spontaneous volunteers
- volunteer coordination system
- decision support
- scheduling optimization model
- linear programming
language:
- iso: eng
oa: '1'
publication: Proceedings of the 15th International Conference on Information Systems
for Crisis Response and Management
status: public
title: Decision Support for the Optimal Coordination of Spontaneous Volunteers in
Disaster Relief
type: conference
user_id: '61579'
year: '2018'
...
---
_id: '17199'
abstract:
- lang: eng
text: 'Research of tutoring in parent-infant interaction has shown that tutors -
when presenting some action - modify both their verbal and manual performance
for the learner (‘motherese’, ‘motionese’). Investigating the sources and effects
of the tutors’ action modifications, we suggest an interactional account of ‘motionese’.
Using video-data from a semi-experimental study in which parents taught their
8 to 11 month old infants how to nest a set of differently sized cups, we found
that the tutors’ action modifications (in particular: high arches) functioned
as an orienting device to guide the infant’s visual attention (gaze). Action modification
and the recipient’s gaze can be seen to have a reciprocal sequential relationship
and to constitute a constant loop of mutual adjustments. Implications are discussed
for developmental research and for robotic ‘Social Learning’. We argue that a
robot system could use on-line feedback strategies (e.g. gaze) to pro-actively
shape a tutor’s action presentation as it emerges.'
author:
- first_name: Karola
full_name: Pitsch, Karola
last_name: Pitsch
- first_name: Anna-Lisa
full_name: Vollmer, Anna-Lisa
last_name: Vollmer
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Jannik
full_name: Fritsch, Jannik
last_name: Fritsch
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: 'Pitsch K, Vollmer A-L, Rohlfing K, Fritsch J, Wrede B. Tutoring in adult-child-interaction:
On the loop of the tutor’s action modification and the recipient’s gaze. Interaction
Studies. 2014;15(1):55-98. doi:10.1075/is.15.1.03pit'
apa: 'Pitsch, K., Vollmer, A.-L., Rohlfing, K., Fritsch, J., & Wrede, B. (2014).
Tutoring in adult-child-interaction: On the loop of the tutor’s action modification
and the recipient’s gaze. Interaction Studies, 15(1), 55–98. https://doi.org/10.1075/is.15.1.03pit'
bibtex: '@article{Pitsch_Vollmer_Rohlfing_Fritsch_Wrede_2014, title={Tutoring in
adult-child-interaction: On the loop of the tutor’s action modification and the
recipient’s gaze}, volume={15}, DOI={10.1075/is.15.1.03pit},
number={1}, journal={Interaction Studies}, publisher={John Benjamins Publishing
Company}, author={Pitsch, Karola and Vollmer, Anna-Lisa and Rohlfing, Katharina
and Fritsch, Jannik and Wrede, Britta}, year={2014}, pages={55–98} }'
chicago: 'Pitsch, Karola, Anna-Lisa Vollmer, Katharina Rohlfing, Jannik Fritsch,
and Britta Wrede. “Tutoring in Adult-Child-Interaction: On the Loop of the Tutor’s
Action Modification and the Recipient’s Gaze.” Interaction Studies 15,
no. 1 (2014): 55–98. https://doi.org/10.1075/is.15.1.03pit.'
ieee: 'K. Pitsch, A.-L. Vollmer, K. Rohlfing, J. Fritsch, and B. Wrede, “Tutoring
in adult-child-interaction: On the loop of the tutor’s action modification and
the recipient’s gaze,” Interaction Studies, vol. 15, no. 1, pp. 55–98,
2014, doi: 10.1075/is.15.1.03pit.'
mla: 'Pitsch, Karola, et al. “Tutoring in Adult-Child-Interaction: On the Loop of
the Tutor’s Action Modification and the Recipient’s Gaze.” Interaction Studies,
vol. 15, no. 1, John Benjamins Publishing Company, 2014, pp. 55–98, doi:10.1075/is.15.1.03pit.'
short: K. Pitsch, A.-L. Vollmer, K. Rohlfing, J. Fritsch, B. Wrede, Interaction
Studies 15 (2014) 55–98.
date_created: 2020-06-24T13:01:17Z
date_updated: 2023-02-01T16:10:52Z
department:
- _id: '749'
doi: 10.1075/is.15.1.03pit
intvolume: ' 15'
issue: '1'
keyword:
- conversation analysis
- interactional coordination
- adult-child-interaction
- feedback
- gaze
- quantification
- social learning
- motionese
- tutoring
language:
- iso: eng
page: 55-98
publication: Interaction Studies
publication_identifier:
issn:
- 1572-0381
publisher: John Benjamins Publishing Company
status: public
title: 'Tutoring in adult-child-interaction: On the loop of the tutor''s action modification
and the recipient''s gaze'
type: journal_article
user_id: '14931'
volume: 15
year: '2014'
...