---
_id: '29672'
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
citation:
ama: 'Schneider SB. Network and Service Coordination: Conventional and Machine
Learning Approaches".; 2022. doi:10.17619/UNIPB/1-1276 '
apa: 'Schneider, S. B. (2022). Network and Service Coordination: Conventional
and Machine Learning Approaches". https://doi.org/10.17619/UNIPB/1-1276 '
bibtex: '@book{Schneider_2022, title={Network and Service Coordination: Conventional
and Machine Learning Approaches"}, DOI={10.17619/UNIPB/1-1276 }, author={Schneider, Stefan Balthasar}, year={2022}
}'
chicago: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional
and Machine Learning Approaches", 2022. https://doi.org/10.17619/UNIPB/1-1276 .'
ieee: 'S. B. Schneider, Network and Service Coordination: Conventional and Machine
Learning Approaches". 2022.'
mla: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional
and Machine Learning Approaches". 2022, doi:10.17619/UNIPB/1-1276 .'
short: 'S.B. Schneider, Network and Service Coordination: Conventional and Machine
Learning Approaches", 2022.'
date_created: 2022-01-31T07:08:47Z
date_updated: 2022-02-18T08:17:36Z
department:
- _id: '75'
doi: '10.17619/UNIPB/1-1276 '
language:
- iso: eng
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
supervisor:
- first_name: Karl
full_name: Holger, Karl
last_name: Holger
title: 'Network and Service Coordination: Conventional and Machine Learning Approaches"'
type: dissertation
user_id: '15504'
year: '2022'
...
---
_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: '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: IEEE/IFIP Network Operations and Management Symposium
(NOMS). IEEE; 2022.'
apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network
and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and
Management Symposium (NOMS). 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 IEEE/IFIP
Network Operations and Management Symposium (NOMS). 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.” IEEE/IFIP Network Operations and Management Symposium (NOMS),
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: 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: '33854'
abstract:
- lang: eng
text: "Macrodiversity is a key technique to increase the capacity of mobile networks.
It can be realized using coordinated multipoint (CoMP), simultaneously connecting
users to multiple overlapping cells. Selecting which users to serve by how many
and which cells is NP-hard but needs to happen continuously in real time as users
move and channel state changes. Existing approaches often require strict assumptions
about or perfect knowledge of the underlying radio system, its resource allocation
scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead,
we propose three novel self-learning and self-adapting approaches using model-free
deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages
central observations and control of all users to select cells almost optimally.
DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and
highly scalable coordination. All three approaches learn from experience and self-adapt
to varying scenarios, reaching 2x higher Quality of Experience than other approaches.
They have very few built-in assumptions and do not need prior system knowledge,
making them more robust to change and better applicable in practice than existing
approaches."
author:
- 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
- first_name: Ramin
full_name: Khalili, Ramin
last_name: Khalili
- first_name: Artur
full_name: Hecker, Artur
last_name: Hecker
citation:
ama: 'Schneider SB, Karl H, Khalili R, Hecker A. DeepCoMP: Coordinated Multipoint
Using Multi-Agent Deep Reinforcement Learning.; 2021.'
apa: 'Schneider, S. B., Karl, H., Khalili, R., & Hecker, A. (2021). DeepCoMP:
Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.'
bibtex: '@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated
Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider,
Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021}
}'
chicago: 'Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker.
DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning,
2021.'
ieee: 'S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, DeepCoMP: Coordinated
Multipoint Using Multi-Agent Deep Reinforcement Learning. 2021.'
mla: 'Schneider, Stefan Balthasar, et al. DeepCoMP: Coordinated Multipoint Using
Multi-Agent Deep Reinforcement Learning. 2021.'
short: 'S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint
Using Multi-Agent Deep Reinforcement Learning, 2021.'
date_created: 2022-10-20T16:44:19Z
date_updated: 2022-11-18T09:59:27Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2022-10-20T16:41:10Z
date_updated: 2022-10-20T16:41:10Z
file_id: '33855'
file_name: preprint.pdf
file_size: 2521656
relation: main_file
file_date_updated: 2022-10-20T16:41:10Z
has_accepted_license: '1'
keyword:
- mobility management
- coordinated multipoint
- CoMP
- cell selection
- resource management
- reinforcement learning
- multi agent
- MARL
- self-learning
- self-adaptation
- QoE
language:
- iso: eng
oa: '1'
project:
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
- _id: '1'
name: 'SFB 901: SFB 901'
status: public
title: 'DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning'
type: working_paper
user_id: '477'
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: '16219'
abstract:
- lang: eng
text: "Network function virtualization (NFV) proposes\r\nto replace physical middleboxes
with more flexible virtual\r\nnetwork functions (VNFs). To dynamically adjust
to everchanging\r\ntraffic demands, VNFs have to be instantiated and\r\ntheir
allocated resources have to be adjusted on demand.\r\nDeciding the amount of allocated
resources is non-trivial.\r\nExisting optimization approaches often assume fixed
resource\r\nrequirements for each VNF instance. However, this can easily\r\nlead
to either waste of resources or bad service quality if too\r\nmany or too few
resources are allocated.\r\n\r\nTo solve this problem, we train machine learning
models\r\non real VNF data, containing measurements of performance\r\nand resource
requirements. For each VNF, the trained models\r\ncan then accurately predict
the required resources to handle\r\na certain traffic load. We integrate these
machine learning\r\nmodels into an algorithm for joint VNF scaling and placement\r\nand
evaluate their impact on resulting VNF placements. Our\r\nevaluation based on
real-world data shows that using suitable\r\nmachine learning models effectively
avoids over- and underallocation\r\nof resources, leading to up to 12 times lower
resource\r\nconsumption and better service quality with up to 4.5 times\r\nlower
total delay than using standard fixed resource allocation."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Narayanan Puthenpurayil
full_name: Satheeschandran, Narayanan Puthenpurayil
last_name: Satheeschandran
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Satheeschandran NP, Peuster M, Karl H. Machine Learning for
Dynamic Resource Allocation in Network Function Virtualization. In: IEEE Conference
on Network Softwarization (NetSoft). IEEE; 2020.'
apa: 'Schneider, S. B., Satheeschandran, N. P., Peuster, M., & Karl, H. (2020).
Machine Learning for Dynamic Resource Allocation in Network Function Virtualization.
In IEEE Conference on Network Softwarization (NetSoft). Ghent, Belgium:
IEEE.'
bibtex: '@inproceedings{Schneider_Satheeschandran_Peuster_Karl_2020, title={Machine
Learning for Dynamic Resource Allocation in Network Function Virtualization},
booktitle={IEEE Conference on Network Softwarization (NetSoft)}, publisher={IEEE},
author={Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil
and Peuster, Manuel and Karl, Holger}, year={2020} }'
chicago: Schneider, Stefan Balthasar, Narayanan Puthenpurayil Satheeschandran, Manuel
Peuster, and Holger Karl. “Machine Learning for Dynamic Resource Allocation in
Network Function Virtualization.” In IEEE Conference on Network Softwarization
(NetSoft). IEEE, 2020.
ieee: S. B. Schneider, N. P. Satheeschandran, M. Peuster, and H. Karl, “Machine
Learning for Dynamic Resource Allocation in Network Function Virtualization,”
in IEEE Conference on Network Softwarization (NetSoft), Ghent, Belgium,
2020.
mla: Schneider, Stefan Balthasar, et al. “Machine Learning for Dynamic Resource
Allocation in Network Function Virtualization.” IEEE Conference on Network
Softwarization (NetSoft), IEEE, 2020.
short: 'S.B. Schneider, N.P. Satheeschandran, M. Peuster, H. Karl, in: IEEE Conference
on Network Softwarization (NetSoft), IEEE, 2020.'
conference:
location: Ghent, Belgium
name: IEEE Conference on Network Softwarization (NetSoft)
date_created: 2020-03-03T11:42:22Z
date_updated: 2022-01-06T06:52:46Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2020-03-03T11:42:16Z
date_updated: 2020-03-03T11:42:16Z
file_id: '16220'
file_name: ris_preprint.pdf
file_size: 476590
relation: main_file
file_date_updated: 2020-03-03T11:42:16Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE Conference on Network Softwarization (NetSoft)
publisher: IEEE
status: public
title: Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
type: conference
user_id: '35343'
year: '2020'
...
---
_id: '16222'
author:
- first_name: A.
full_name: Zafeiropoulos, A.
last_name: Zafeiropoulos
- first_name: E.
full_name: Fotopoulou, E.
last_name: Fotopoulou
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: P.
full_name: Gouvas, P.
last_name: Gouvas
- first_name: D.
full_name: Behnke, D.
last_name: Behnke
- first_name: M.
full_name: Müller, M.
last_name: Müller
- first_name: P.
full_name: Bök, P.
last_name: Bök
- first_name: P.
full_name: Trakadas, P.
last_name: Trakadas
- first_name: P.
full_name: Karkazis, P.
last_name: Karkazis
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Zafeiropoulos A, Fotopoulou E, Peuster M, et al. Benchmarking and Profiling
5G Verticals’ Applications: An Industrial IoT Use Case. In: IEEE Conference
on Network Softwarization (NetSoft). ; 2020.'
apa: 'Zafeiropoulos, A., Fotopoulou, E., Peuster, M., Schneider, S. B., Gouvas,
P., Behnke, D., … Karl, H. (2020). Benchmarking and Profiling 5G Verticals’ Applications:
An Industrial IoT Use Case. In IEEE Conference on Network Softwarization (NetSoft).'
bibtex: '@inproceedings{Zafeiropoulos_Fotopoulou_Peuster_Schneider_Gouvas_Behnke_Müller_Bök_Trakadas_Karkazis_et
al._2020, title={Benchmarking and Profiling 5G Verticals’ Applications: An Industrial
IoT Use Case}, booktitle={IEEE Conference on Network Softwarization (NetSoft)},
author={Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider,
Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and
Trakadas, P. and Karkazis, P. and et al.}, year={2020} }'
chicago: 'Zafeiropoulos, A., E. Fotopoulou, Manuel Peuster, Stefan Balthasar Schneider,
P. Gouvas, D. Behnke, M. Müller, et al. “Benchmarking and Profiling 5G Verticals’
Applications: An Industrial IoT Use Case.” In IEEE Conference on Network Softwarization
(NetSoft), 2020.'
ieee: 'A. Zafeiropoulos et al., “Benchmarking and Profiling 5G Verticals’
Applications: An Industrial IoT Use Case,” in IEEE Conference on Network Softwarization
(NetSoft), 2020.'
mla: 'Zafeiropoulos, A., et al. “Benchmarking and Profiling 5G Verticals’ Applications:
An Industrial IoT Use Case.” IEEE Conference on Network Softwarization (NetSoft),
2020.'
short: 'A. Zafeiropoulos, E. Fotopoulou, M. Peuster, S.B. Schneider, P. Gouvas,
D. Behnke, M. Müller, P. Bök, P. Trakadas, P. Karkazis, H. Karl, in: IEEE Conference
on Network Softwarization (NetSoft), 2020.'
date_created: 2020-03-03T11:51:22Z
date_updated: 2022-01-06T06:52:46Z
department:
- _id: '75'
language:
- iso: eng
project:
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
publication: IEEE Conference on Network Softwarization (NetSoft)
status: public
title: 'Benchmarking and Profiling 5G Verticals'' Applications: An Industrial IoT
Use Case'
type: conference
user_id: '35343'
year: '2020'
...
---
_id: '16400'
abstract:
- lang: eng
text: "Softwarization facilitates the introduction of smart\r\nmanufacturing applications
in the industry. Manifold devices\r\nsuch as machine computers, Industrial IoT
devices, tablets,\r\nsmartphones and smart glasses are integrated into factory
networks\r\nto enable shop floor digitalization and big data analysis. To\r\nhandle
the increasing number of devices and the resulting traffic,\r\na flexible and
scalable factory network is necessary which can be\r\nrealized using softwarization
technologies like Network Function\r\nVirtualization (NFV). However, the security
risks increase with\r\nthe increasing number of new devices, so that cyber security
must\r\nalso be considered in NFV-based networks.\r\n\r\nTherefore, extending
our previous work, we showcase threat\r\ndetection using a cloud-native NFV-driven
intrusion detection\r\nsystem (IDS) that is integrated in our industrial-specific
network\r\nservices. As a result of the threat detection, the affected network\r\nservice
is put into quarantine via automatic network reconfiguration.\r\nWe use the 5GTANGO
service platform to deploy our\r\ndeveloped network services on Kubernetes and
to initiate the\r\nnetwork reconfiguration."
author:
- first_name: Marcel
full_name: Müller, Marcel
last_name: Müller
- first_name: Daniel
full_name: Behnke, Daniel
last_name: Behnke
- first_name: Patrick-Benjamin
full_name: Bök, Patrick-Benjamin
last_name: Bök
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Müller M, Behnke D, Bök P-B, Schneider SB, Peuster M, Karl H. Cloud-Native
Threat Detection and Containment for Smart Manufacturing. In: IEEE Conference
on Network Softwarization (NetSoft) Demo Track. Ghent, Belgium: IEEE; 2020.'
apa: 'Müller, M., Behnke, D., Bök, P.-B., Schneider, S. B., Peuster, M., & Karl,
H. (2020). Cloud-Native Threat Detection and Containment for Smart Manufacturing.
In IEEE Conference on Network Softwarization (NetSoft) Demo Track. Ghent,
Belgium: IEEE.'
bibtex: '@inproceedings{Müller_Behnke_Bök_Schneider_Peuster_Karl_2020, place={Ghent,
Belgium}, title={Cloud-Native Threat Detection and Containment for Smart Manufacturing},
booktitle={IEEE Conference on Network Softwarization (NetSoft) Demo Track}, publisher={IEEE},
author={Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider,
Stefan Balthasar and Peuster, Manuel and Karl, Holger}, year={2020} }'
chicago: 'Müller, Marcel, Daniel Behnke, Patrick-Benjamin Bök, Stefan Balthasar
Schneider, Manuel Peuster, and Holger Karl. “Cloud-Native Threat Detection and
Containment for Smart Manufacturing.” In IEEE Conference on Network Softwarization
(NetSoft) Demo Track. Ghent, Belgium: IEEE, 2020.'
ieee: M. Müller, D. Behnke, P.-B. Bök, S. B. Schneider, M. Peuster, and H. Karl,
“Cloud-Native Threat Detection and Containment for Smart Manufacturing,” in IEEE
Conference on Network Softwarization (NetSoft) Demo Track, Ghent, Belgium,
2020.
mla: Müller, Marcel, et al. “Cloud-Native Threat Detection and Containment for Smart
Manufacturing.” IEEE Conference on Network Softwarization (NetSoft) Demo Track,
IEEE, 2020.
short: 'M. Müller, D. Behnke, P.-B. Bök, S.B. Schneider, M. Peuster, H. Karl, in:
IEEE Conference on Network Softwarization (NetSoft) Demo Track, IEEE, Ghent, Belgium,
2020.'
conference:
location: Ghent, Belgium
name: IEEE Conference on Network Softwarization (NetSoft) Demo Track
date_created: 2020-04-03T11:53:00Z
date_updated: 2022-01-06T06:52:50Z
department:
- _id: '75'
language:
- iso: eng
place: Ghent, Belgium
project:
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE Conference on Network Softwarization (NetSoft) Demo Track
publisher: IEEE
status: public
title: Cloud-Native Threat Detection and Containment for Smart Manufacturing
type: conference
user_id: '35343'
year: '2020'
...
---
_id: '3287'
abstract:
- lang: eng
text: "For optimal placement and orchestration of network services, it is crucial\r\nthat
their structure and semantics are specified clearly and comprehensively\r\nand
are available to an orchestrator. Existing specification approaches are\r\neither
ambiguous or miss important aspects regarding the behavior of virtual\r\nnetwork
functions (VNFs) forming a service. We propose to formally and\r\nunambiguously
specify the behavior of these functions and services using\r\nQueuing Petri Nets
(QPNs). QPNs are an established method that allows to\r\nexpress queuing, synchronization,
stochastically distributed processing delays,\r\nand changing traffic volume and
characteristics at each VNF. With QPNs,\r\nmultiple VNFs can be connected to complete
network services in any structure,\r\neven specifying bidirectional network services
containing loops.\r\n We discuss how management and orchestration systems can
benefit from our\r\nclear and comprehensive specification approach, leading to
better placement of\r\nVNFs and improved Quality of Service. Another benefit of
formally specifying\r\nnetwork services with QPNs are diverse analysis options,
which allow valuable\r\ninsights such as the distribution of end-to-end delay.
We propose a tool-based\r\nworkflow that supports the specification of network
services and the automatic\r\ngeneration of corresponding simulation code to enable
an in-depth analysis of\r\ntheir behavior and performance."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Schneider SB, Sharma A, Karl H, Wehrheim H. Specifying and Analyzing Virtual
Network Services Using Queuing Petri Nets. In: 2019 IFIP/IEEE International
Symposium on Integrated Network Management (IM). Washington, DC, USA: IFIP;
2019:116--124.'
apa: 'Schneider, S. B., Sharma, A., Karl, H., & Wehrheim, H. (2019). Specifying
and Analyzing Virtual Network Services Using Queuing Petri Nets. In 2019 IFIP/IEEE
International Symposium on Integrated Network Management (IM) (pp. 116--124).
Washington, DC, USA: IFIP.'
bibtex: '@inproceedings{Schneider_Sharma_Karl_Wehrheim_2019, place={Washington,
DC, USA}, title={Specifying and Analyzing Virtual Network Services Using Queuing
Petri Nets}, booktitle={2019 IFIP/IEEE International Symposium on Integrated Network
Management (IM)}, publisher={IFIP}, author={Schneider, Stefan Balthasar and Sharma,
Arnab and Karl, Holger and Wehrheim, Heike}, year={2019}, pages={116--124} }'
chicago: 'Schneider, Stefan Balthasar, Arnab Sharma, Holger Karl, and Heike Wehrheim.
“Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets.”
In 2019 IFIP/IEEE International Symposium on Integrated Network Management
(IM), 116--124. Washington, DC, USA: IFIP, 2019.'
ieee: S. B. Schneider, A. Sharma, H. Karl, and H. Wehrheim, “Specifying and Analyzing
Virtual Network Services Using Queuing Petri Nets,” in 2019 IFIP/IEEE International
Symposium on Integrated Network Management (IM), Washington, DC, USA, 2019,
pp. 116--124.
mla: Schneider, Stefan Balthasar, et al. “Specifying and Analyzing Virtual Network
Services Using Queuing Petri Nets.” 2019 IFIP/IEEE International Symposium
on Integrated Network Management (IM), IFIP, 2019, pp. 116--124.
short: 'S.B. Schneider, A. Sharma, H. Karl, H. Wehrheim, in: 2019 IFIP/IEEE International
Symposium on Integrated Network Management (IM), IFIP, Washington, DC, USA, 2019,
pp. 116--124.'
conference:
end_date: 2019-04-12
location: Washington, DC, USA
name: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM)
start_date: 2019-04-08
date_created: 2018-06-18T15:23:18Z
date_updated: 2022-01-06T06:59:09Z
ddc:
- '040'
department:
- _id: '77'
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2019-01-07T12:38:35Z
date_updated: 2019-01-07T12:38:35Z
file_id: '6504'
file_name: ris_preprint.pdf
file_size: 497528
relation: main_file
file_date_updated: 2019-01-07T12:38:35Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://dl.ifip.org/db/conf/im/im2019/188490.pdf
oa: '1'
page: 116--124
place: Washington, DC, USA
project:
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
- _id: '25'
call_identifier: 5G PPP Phase 1
grant_number: '671517'
name: 'SONATA NFV: Agile Service Development and Orchestration in 5G Virtualized
Networks'
publication: 2019 IFIP/IEEE International Symposium on Integrated Network Management
(IM)
publisher: IFIP
status: public
title: Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets
type: conference
user_id: '35343'
year: '2019'
...
---
_id: '9270'
abstract:
- lang: eng
text: "As 5G and network function virtualization (NFV) are maturing, it becomes
crucial to demonstrate their feasibility and benefits by means of vertical scenarios.
While 5GPPP has identified smart manufacturing as one of the most important vertical
industries, there is still a lack of specific, practical use cases. \r\n\r\nUsing
the experience from a large-scale manufacturing company, Weidm{\\\"u}ller Group,
we present a detailed use case that reflects the needs of real-world manufacturers.
We also propose an architecture with specific network services and virtual network
functions (VNFs) that realize the use case in practice. As a proof of concept,
we implement the required services and deploy them on an emulation-based prototyping
platform. Our experimental results indicate that a fully virtualized smart manufacturing
use case is not only feasible but also reduces machine interconnection and configuration
time and thus improves productivity by orders of magnitude."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Daniel
full_name: Behnke, Daniel
last_name: Behnke
- first_name: Müller
full_name: Marcel, Müller
last_name: Marcel
- first_name: Patrick-Benjamin
full_name: Bök, Patrick-Benjamin
last_name: Bök
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Peuster M, Behnke D, Marcel M, Bök P-B, Karl H. Putting 5G into
Production: Realizing a Smart Manufacturing Vertical Scenario. In: European
Conference on Networks and Communications (EuCNC). Valencia, Spain: IEEE;
2019. doi:10.1109/eucnc.2019.8802016'
apa: 'Schneider, S. B., Peuster, M., Behnke, D., Marcel, M., Bök, P.-B., & Karl,
H. (2019). Putting 5G into Production: Realizing a Smart Manufacturing Vertical
Scenario. In European Conference on Networks and Communications (EuCNC).
Valencia, Spain: IEEE. https://doi.org/10.1109/eucnc.2019.8802016'
bibtex: '@inproceedings{Schneider_Peuster_Behnke_Marcel_Bök_Karl_2019, place={Valencia,
Spain}, title={Putting 5G into Production: Realizing a Smart Manufacturing Vertical
Scenario}, DOI={10.1109/eucnc.2019.8802016},
booktitle={European Conference on Networks and Communications (EuCNC)}, publisher={IEEE},
author={Schneider, Stefan Balthasar and Peuster, Manuel and Behnke, Daniel and
Marcel, Müller and Bök, Patrick-Benjamin and Karl, Holger}, year={2019} }'
chicago: 'Schneider, Stefan Balthasar, Manuel Peuster, Daniel Behnke, Müller Marcel,
Patrick-Benjamin Bök, and Holger Karl. “Putting 5G into Production: Realizing
a Smart Manufacturing Vertical Scenario.” In European Conference on Networks
and Communications (EuCNC). Valencia, Spain: IEEE, 2019. https://doi.org/10.1109/eucnc.2019.8802016.'
ieee: 'S. B. Schneider, M. Peuster, D. Behnke, M. Marcel, P.-B. Bök, and H. Karl,
“Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario,”
in European Conference on Networks and Communications (EuCNC), 2019.'
mla: 'Schneider, Stefan Balthasar, et al. “Putting 5G into Production: Realizing
a Smart Manufacturing Vertical Scenario.” European Conference on Networks and
Communications (EuCNC), IEEE, 2019, doi:10.1109/eucnc.2019.8802016.'
short: 'S.B. Schneider, M. Peuster, D. Behnke, M. Marcel, P.-B. Bök, H. Karl, in:
European Conference on Networks and Communications (EuCNC), IEEE, Valencia, Spain,
2019.'
date_created: 2019-04-23T09:27:06Z
date_updated: 2022-01-06T07:04:12Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/eucnc.2019.8802016
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2019-04-23T09:29:49Z
date_updated: 2019-12-12T09:15:57Z
file_id: '9272'
file_name: preprint_ris_with_header.pdf
file_size: 374397
relation: main_file
file_date_updated: 2019-12-12T09:15:57Z
has_accepted_license: '1'
keyword:
- 5g
- vertical
- smart manufacturing
- nfv
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/8802016
oa: '1'
place: Valencia, Spain
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
publication: European Conference on Networks and Communications (EuCNC)
publisher: IEEE
status: public
title: 'Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario'
type: conference
user_id: '35343'
year: '2019'
...
---
_id: '8792'
abstract:
- lang: eng
text: "5G together with software defined networking (SDN) and network function virtualisation
(NFV) will enable a wide variety of vertical use cases. One of them is the smart
man- ufacturing case which utilises 5G networks to interconnect production machines,
machine parks, and factory sites to enable new possibilities in terms of flexibility,
automation, and novel applications (industry 4.0). However, the availability of
realistic and practical proof-of-concepts for those smart manufacturing scenarios
is still limited.\r\nThis demo fills this gap by not only showing a real-world
smart manufacturing application entirely implemented using NFV concepts, but also
a lightweight prototyping framework that simplifies the realisation of vertical
NFV proof-of-concepts. Dur- ing the demo, we show how an NFV-based smart manufacturing
scenario can be specified, on-boarded, and instantiated before we demonstrate
how the presented NFV services simplify machine data collection, aggregation,
and analysis."
author:
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Daniel
full_name: Behnke, Daniel
last_name: Behnke
- first_name: Marcel
full_name: Müller, Marcel
last_name: Müller
- first_name: Patrick-Benjamin
full_name: Bök, Patrick-Benjamin
last_name: Bök
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Peuster M, Schneider SB, Behnke D, Müller M, Bök P-B, Karl H. Prototyping
and Demonstrating 5G Verticals: The Smart Manufacturing Case. In: 5th IEEE
International Conference on Network Softwarization (NetSoft 2019). Paris;
2019. doi:10.1109/NETSOFT.2019.8806685'
apa: 'Peuster, M., Schneider, S. B., Behnke, D., Müller, M., Bök, P.-B., & Karl,
H. (2019). Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing
Case. In 5th IEEE International Conference on Network Softwarization (NetSoft
2019). Paris. https://doi.org/10.1109/NETSOFT.2019.8806685'
bibtex: '@inproceedings{Peuster_Schneider_Behnke_Müller_Bök_Karl_2019, place={Paris},
title={Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case},
DOI={10.1109/NETSOFT.2019.8806685},
booktitle={5th IEEE International Conference on Network Softwarization (NetSoft
2019)}, author={Peuster, Manuel and Schneider, Stefan Balthasar and Behnke, Daniel
and Müller, Marcel and Bök, Patrick-Benjamin and Karl, Holger}, year={2019} }'
chicago: 'Peuster, Manuel, Stefan Balthasar Schneider, Daniel Behnke, Marcel Müller,
Patrick-Benjamin Bök, and Holger Karl. “Prototyping and Demonstrating 5G Verticals:
The Smart Manufacturing Case.” In 5th IEEE International Conference on Network
Softwarization (NetSoft 2019). Paris, 2019. https://doi.org/10.1109/NETSOFT.2019.8806685.'
ieee: 'M. Peuster, S. B. Schneider, D. Behnke, M. Müller, P.-B. Bök, and H. Karl,
“Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case,” in
5th IEEE International Conference on Network Softwarization (NetSoft 2019),
Paris, 2019.'
mla: 'Peuster, Manuel, et al. “Prototyping and Demonstrating 5G Verticals: The Smart
Manufacturing Case.” 5th IEEE International Conference on Network Softwarization
(NetSoft 2019), 2019, doi:10.1109/NETSOFT.2019.8806685.'
short: 'M. Peuster, S.B. Schneider, D. Behnke, M. Müller, P.-B. Bök, H. Karl, in:
5th IEEE International Conference on Network Softwarization (NetSoft 2019), Paris,
2019.'
conference:
end_date: 2019-06-28
location: Paris
name: 5th IEEE International Conference on Network Softwarization (NetSoft 2019)
start_date: 2019-06-24
date_created: 2019-04-01T13:37:05Z
date_updated: 2022-01-06T07:04:01Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/NETSOFT.2019.8806685
file:
- access_level: open_access
content_type: application/pdf
creator: peuster
date_created: 2019-04-01T13:46:18Z
date_updated: 2019-04-01T13:46:18Z
file_id: '8794'
file_name: main_for_ris.pdf
file_size: 1693793
relation: main_file
file_date_updated: 2019-04-01T13:46:18Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- url: https://doi.org/10.1109/NETSOFT.2019.8806685
oa: '1'
place: Paris
project:
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: 5th IEEE International Conference on Network Softwarization (NetSoft
2019)
status: public
title: 'Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case'
type: conference
user_id: '13271'
year: '2019'
...
---
_id: '9824'
author:
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Mengxuan
full_name: Zhao, Mengxuan
last_name: Zhao
- first_name: George
full_name: Xilouris, George
last_name: Xilouris
- first_name: Panagiotis
full_name: Trakadas, Panagiotis
last_name: Trakadas
- first_name: Felipe
full_name: Vicens, Felipe
last_name: Vicens
- first_name: Wouter
full_name: Tavernier, Wouter
last_name: Tavernier
- first_name: Thomas
full_name: Soenen, Thomas
last_name: Soenen
- first_name: Ricard
full_name: Vilalta, Ricard
last_name: Vilalta
- first_name: George
full_name: Andreou, George
last_name: Andreou
- first_name: Dimosthenis
full_name: Kyriazis, Dimosthenis
last_name: Kyriazis
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: Peuster M, Schneider SB, Zhao M, et al. Introducing Automated Verification
and Validation for Virtualized Network Functions and Services. IEEE Communications
Magazine. 2019:96-102. doi:10.1109/mcom.2019.1800873
apa: Peuster, M., Schneider, S. B., Zhao, M., Xilouris, G., Trakadas, P., Vicens,
F., … Karl, H. (2019). Introducing Automated Verification and Validation for Virtualized
Network Functions and Services. IEEE Communications Magazine, 96–102. https://doi.org/10.1109/mcom.2019.1800873
bibtex: '@article{Peuster_Schneider_Zhao_Xilouris_Trakadas_Vicens_Tavernier_Soenen_Vilalta_Andreou_et
al._2019, title={Introducing Automated Verification and Validation for Virtualized
Network Functions and Services}, DOI={10.1109/mcom.2019.1800873},
journal={IEEE Communications Magazine}, author={Peuster, Manuel and Schneider,
Stefan Balthasar and Zhao, Mengxuan and Xilouris, George and Trakadas, Panagiotis
and Vicens, Felipe and Tavernier, Wouter and Soenen, Thomas and Vilalta, Ricard
and Andreou, George and et al.}, year={2019}, pages={96–102} }'
chicago: Peuster, Manuel, Stefan Balthasar Schneider, Mengxuan Zhao, George Xilouris,
Panagiotis Trakadas, Felipe Vicens, Wouter Tavernier, et al. “Introducing Automated
Verification and Validation for Virtualized Network Functions and Services.” IEEE
Communications Magazine, 2019, 96–102. https://doi.org/10.1109/mcom.2019.1800873.
ieee: M. Peuster et al., “Introducing Automated Verification and Validation
for Virtualized Network Functions and Services,” IEEE Communications Magazine,
pp. 96–102, 2019.
mla: Peuster, Manuel, et al. “Introducing Automated Verification and Validation
for Virtualized Network Functions and Services.” IEEE Communications Magazine,
2019, pp. 96–102, doi:10.1109/mcom.2019.1800873.
short: M. Peuster, S.B. Schneider, M. Zhao, G. Xilouris, P. Trakadas, F. Vicens,
W. Tavernier, T. Soenen, R. Vilalta, G. Andreou, D. Kyriazis, H. Karl, IEEE Communications
Magazine (2019) 96–102.
date_created: 2019-05-16T09:09:16Z
date_updated: 2022-01-06T07:04:23Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/mcom.2019.1800873
file:
- access_level: open_access
content_type: application/pdf
creator: peuster
date_created: 2019-05-16T09:13:40Z
date_updated: 2019-05-16T09:13:40Z
description: |+
Preprint of original article: M. Peuster et al., "Introducing Automated Verification and Validation for Virtualized Network Functions and Services," in IEEE Communications Magazine, vol. 57, no. 5, pp. 96-102, May 2019.
doi: 10.1109/MCOM.2019.1800873
file_id: '9825'
file_name: main_for_ris.pdf
file_size: 1735036
relation: main_file
title: Introducing Automated Verification and Validation for Virtualized Network
Functions and Services
file_date_updated: 2019-05-16T09:13:40Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/8713807
oa: '1'
page: 96-102
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE Communications Magazine
publication_identifier:
issn:
- 0163-6804
- 1558-1896
publication_status: published
status: public
title: Introducing Automated Verification and Validation for Virtualized Network Functions
and Services
type: journal_article
user_id: '13271'
year: '2019'
...
...
---
_id: '15369'
author:
- first_name: Marcel
full_name: Müller, Marcel
last_name: Müller
- first_name: Daniel
full_name: Behnke, Daniel
last_name: Behnke
- first_name: Patrick-Benjamin
full_name: Bök, Patrick-Benjamin
last_name: Bök
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- 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: 'Müller M, Behnke D, Bök P-B, Peuster M, Schneider SB, Karl H. 5G as Key Technology
for Networked Factories: Application of Vertical-specific Network Services for
Enabling Flexible Smart Manufacturing. In: IEEE 17th International Conference
on Industrial Informatics (IEEE-INDIN). Helsinki: IEEE; 2019.'
apa: 'Müller, M., Behnke, D., Bök, P.-B., Peuster, M., Schneider, S. B., & Karl,
H. (2019). 5G as Key Technology for Networked Factories: Application of Vertical-specific
Network Services for Enabling Flexible Smart Manufacturing. In IEEE 17th International
Conference on Industrial Informatics (IEEE-INDIN). Helsinki: IEEE.'
bibtex: '@inproceedings{Müller_Behnke_Bök_Peuster_Schneider_Karl_2019, place={Helsinki},
title={5G as Key Technology for Networked Factories: Application of Vertical-specific
Network Services for Enabling Flexible Smart Manufacturing}, booktitle={IEEE 17th
International Conference on Industrial Informatics (IEEE-INDIN)}, publisher={IEEE},
author={Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Peuster,
Manuel and Schneider, Stefan Balthasar and Karl, Holger}, year={2019} }'
chicago: 'Müller, Marcel, Daniel Behnke, Patrick-Benjamin Bök, Manuel Peuster, Stefan
Balthasar Schneider, and Holger Karl. “5G as Key Technology for Networked Factories:
Application of Vertical-Specific Network Services for Enabling Flexible Smart
Manufacturing.” In IEEE 17th International Conference on Industrial Informatics
(IEEE-INDIN). Helsinki: IEEE, 2019.'
ieee: 'M. Müller, D. Behnke, P.-B. Bök, M. Peuster, S. B. Schneider, and H. Karl,
“5G as Key Technology for Networked Factories: Application of Vertical-specific
Network Services for Enabling Flexible Smart Manufacturing,” in IEEE 17th International
Conference on Industrial Informatics (IEEE-INDIN), 2019.'
mla: 'Müller, Marcel, et al. “5G as Key Technology for Networked Factories: Application
of Vertical-Specific Network Services for Enabling Flexible Smart Manufacturing.”
IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN),
IEEE, 2019.'
short: 'M. Müller, D. Behnke, P.-B. Bök, M. Peuster, S.B. Schneider, H. Karl, in:
IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN), IEEE,
Helsinki, 2019.'
date_created: 2019-12-18T07:27:24Z
date_updated: 2022-01-06T06:52:21Z
department:
- _id: '75'
language:
- iso: eng
place: Helsinki
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
publication: IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN)
publisher: IEEE
status: public
title: '5G as Key Technology for Networked Factories: Application of Vertical-specific
Network Services for Enabling Flexible Smart Manufacturing'
type: conference
user_id: '13271'
year: '2019'
...
---
_id: '15371'
abstract:
- lang: eng
text: "More and more management and orchestration approaches for (software) networks
are based on machine learning paradigms and solutions. These approaches depend
not only on their program code to operate properly, but also require enough input
data to train their internal models. However, such training data is barely available
for the software networking domain and most presented solutions rely on their
own, sometimes not even published, data sets. This makes it hard, or even infeasible,
to reproduce and compare many of the existing solutions. As a result, it ultimately
slows down the adoption of machine learning approaches in softwarised networks.
To this end, we introduce the \"softwarised network data zoo\" (SNDZoo), an open
collection of software networking data sets aiming to streamline and ease machine
learning research in the software networking domain. We present a general methodology
to collect, archive, and publish those data sets for use by other researches and,
as an example, eight initial data sets, focusing on the performance of virtualised
network functions.\r\n"
author:
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- 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: 'Peuster M, Schneider SB, Karl H. The Softwarised Network Data Zoo. In: IEEE/IFIP
15th International Conference on Network and Service Management (CNSM). Halifax:
IEEE/IFIP; 2019.'
apa: 'Peuster, M., Schneider, S. B., & Karl, H. (2019). The Softwarised Network
Data Zoo. In IEEE/IFIP 15th International Conference on Network and Service
Management (CNSM). Halifax: IEEE/IFIP.'
bibtex: '@inproceedings{Peuster_Schneider_Karl_2019, place={Halifax}, title={The
Softwarised Network Data Zoo}, booktitle={IEEE/IFIP 15th International Conference
on Network and Service Management (CNSM)}, publisher={IEEE/IFIP}, author={Peuster,
Manuel and Schneider, Stefan Balthasar and Karl, Holger}, year={2019} }'
chicago: 'Peuster, Manuel, Stefan Balthasar Schneider, and Holger Karl. “The Softwarised
Network Data Zoo.” In IEEE/IFIP 15th International Conference on Network and
Service Management (CNSM). Halifax: IEEE/IFIP, 2019.'
ieee: M. Peuster, S. B. Schneider, and H. Karl, “The Softwarised Network Data Zoo,”
in IEEE/IFIP 15th International Conference on Network and Service Management
(CNSM), 2019.
mla: Peuster, Manuel, et al. “The Softwarised Network Data Zoo.” IEEE/IFIP 15th
International Conference on Network and Service Management (CNSM), IEEE/IFIP,
2019.
short: 'M. Peuster, S.B. Schneider, H. Karl, in: IEEE/IFIP 15th International Conference
on Network and Service Management (CNSM), IEEE/IFIP, Halifax, 2019.'
date_created: 2019-12-18T07:30:45Z
date_updated: 2022-01-06T06:52:21Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: peuster
date_created: 2019-12-18T08:10:23Z
date_updated: 2019-12-18T08:10:23Z
file_id: '15377'
file_name: main_for_ris.pdf
file_size: 515208
relation: main_file
file_date_updated: 2019-12-18T08:10:23Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://dl.ifip.org/db/conf/cnsm/cnsm2019/1570555677.pdf
oa: '1'
place: Halifax
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
publication: IEEE/IFIP 15th International Conference on Network and Service Management
(CNSM)
publisher: IEEE/IFIP
status: public
title: The Softwarised Network Data Zoo
type: conference
user_id: '13271'
year: '2019'
...
---
_id: '15372'
author:
- first_name: Askhat
full_name: Nuriddinov, Askhat
last_name: Nuriddinov
- first_name: Wouter
full_name: Tavernier, Wouter
last_name: Tavernier
- first_name: Didier
full_name: Colle, Didier
last_name: Colle
- first_name: Mario
full_name: Pickavet, Mario
last_name: Pickavet
- first_name: Manuel
full_name: Peuster, Manuel
id: '13271'
last_name: Peuster
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
citation:
ama: 'Nuriddinov A, Tavernier W, Colle D, Pickavet M, Peuster M, Schneider SB. Reproducible
Functional Tests for Multi-scale Network Services. In: IEEE Conference on
Network Function Virtualization and Software Defined Networks (NFV-SDN). Dallas:
IEEE; 2019.'
apa: 'Nuriddinov, A., Tavernier, W., Colle, D., Pickavet, M., Peuster, M., &
Schneider, S. B. (2019). Reproducible Functional Tests for Multi-scale Network
Services. In IEEE Conference on Network Function Virtualization and Software
Defined Networks (NFV-SDN). Dallas: IEEE.'
bibtex: '@inproceedings{Nuriddinov_Tavernier_Colle_Pickavet_Peuster_Schneider_2019,
place={Dallas}, title={Reproducible Functional Tests for Multi-scale Network Services},
booktitle={ IEEE Conference on Network Function Virtualization and Software Defined
Networks (NFV-SDN)}, publisher={IEEE}, author={Nuriddinov, Askhat and Tavernier,
Wouter and Colle, Didier and Pickavet, Mario and Peuster, Manuel and Schneider,
Stefan Balthasar}, year={2019} }'
chicago: 'Nuriddinov, Askhat, Wouter Tavernier, Didier Colle, Mario Pickavet, Manuel
Peuster, and Stefan Balthasar Schneider. “Reproducible Functional Tests for Multi-Scale
Network Services.” In IEEE Conference on Network Function Virtualization and
Software Defined Networks (NFV-SDN). Dallas: IEEE, 2019.'
ieee: A. Nuriddinov, W. Tavernier, D. Colle, M. Pickavet, M. Peuster, and S. B.
Schneider, “Reproducible Functional Tests for Multi-scale Network Services,” in
IEEE Conference on Network Function Virtualization and Software Defined Networks
(NFV-SDN), 2019.
mla: Nuriddinov, Askhat, et al. “Reproducible Functional Tests for Multi-Scale Network
Services.” IEEE Conference on Network Function Virtualization and Software
Defined Networks (NFV-SDN), IEEE, 2019.
short: 'A. Nuriddinov, W. Tavernier, D. Colle, M. Pickavet, M. Peuster, S.B. Schneider,
in: IEEE Conference on Network Function Virtualization and Software Defined Networks
(NFV-SDN), IEEE, Dallas, 2019.'
date_created: 2019-12-18T07:36:04Z
date_updated: 2022-01-06T06:52:21Z
department:
- _id: '75'
language:
- iso: eng
place: Dallas
project:
- _id: '28'
grant_number: '761493'
name: 5G Development and validation platform for global industry-specific network
services and Apps
publication: ' IEEE Conference on Network Function Virtualization and Software Defined
Networks (NFV-SDN)'
publisher: IEEE
status: public
title: Reproducible Functional Tests for Multi-scale Network Services
type: conference
user_id: '13271'
year: '2019'
...