---
_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: '20125'
abstract:
- lang: eng
text: Datacenter applications have different resource requirements from network
and developing flow scheduling heuristics for every workload is practically infeasible.
In this paper, we show that deep reinforcement learning (RL) can be used to efficiently
learn flow scheduling policies for different workloads without manual feature
engineering. Specifically, we present LFS, which learns to optimize a high-level
performance objective, e.g., maximize the number of flow admissions while meeting
the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling
policy on continuous online flow arrivals. The evaluation results show that the
trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling
heuristics under varying network load.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Learning Flow Scheduling. In: 2021 IEEE 18th Annual
Consumer Communications & Networking Conference (CCNC). IEEE Computer
Society. doi:https://doi.org/10.1109/CCNC49032.2021.9369514'
apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Flow Scheduling. In 2021 IEEE
18th Annual Consumer Communications & Networking Conference (CCNC). Las
Vegas, USA: IEEE Computer Society. https://doi.org/10.1109/CCNC49032.2021.9369514'
bibtex: '@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={https://doi.org/10.1109/CCNC49032.2021.9369514},
booktitle={2021 IEEE 18th Annual Consumer Communications & Networking Conference
(CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger}
}'
chicago: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In 2021
IEEE 18th Annual Consumer Communications & Networking Conference (CCNC).
IEEE Computer Society, n.d. https://doi.org/10.1109/CCNC49032.2021.9369514.
ieee: A. Hasnain and H. Karl, “Learning Flow Scheduling,” in 2021 IEEE 18th Annual
Consumer Communications & Networking Conference (CCNC), Las Vegas, USA.
mla: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” 2021 IEEE 18th
Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer
Society, doi:https://doi.org/10.1109/CCNC49032.2021.9369514.
short: 'A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &
Networking Conference (CCNC), IEEE Computer Society, n.d.'
conference:
end_date: 2021-01-12
location: Las Vegas, USA
name: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
start_date: 2021-01-09
date_created: 2020-10-19T14:27:17Z
date_updated: 2022-01-06T06:54:20Z
ddc:
- '000'
department:
- _id: '75'
doi: https://doi.org/10.1109/CCNC49032.2021.9369514
keyword:
- Flow scheduling
- Deadlines
- Reinforcement learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9369514
project:
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '1'
name: SFB 901
publication: 2021 IEEE 18th Annual Consumer Communications & Networking Conference
(CCNC)
publication_status: accepted
publisher: IEEE Computer Society
status: public
title: Learning Flow Scheduling
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '27503'
author:
- first_name: Asif
full_name: Hasnain, Asif
last_name: Hasnain
citation:
ama: Hasnain A. Automating Network Resource Allocation for Coflows with Deadlines.;
2021. doi:10.17619/UNIPB/1-1241
apa: Hasnain, A. (2021). Automating Network Resource Allocation for Coflows with
Deadlines. https://doi.org/10.17619/UNIPB/1-1241
bibtex: '@book{Hasnain_2021, title={Automating Network Resource Allocation for Coflows
with Deadlines}, DOI={10.17619/UNIPB/1-1241
}, author={Hasnain, Asif}, year={2021} }'
chicago: Hasnain, Asif. Automating Network Resource Allocation for Coflows with
Deadlines, 2021. https://doi.org/10.17619/UNIPB/1-1241
.
ieee: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines.
2021.
mla: Hasnain, Asif. Automating Network Resource Allocation for Coflows with Deadlines.
2021, doi:10.17619/UNIPB/1-1241
.
short: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines,
2021.
date_created: 2021-11-16T13:05:12Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '75'
doi: '10.17619/UNIPB/1-1241 '
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
status: public
supervisor:
- first_name: Holger
full_name: Karl, Holger
last_name: Karl
title: Automating Network Resource Allocation for Coflows with Deadlines
type: dissertation
user_id: '15504'
year: '2021'
...
---
_id: '21005'
abstract:
- lang: eng
text: Data-parallel applications are developed using different data programming
models, e.g., MapReduce, partition/aggregate. These models represent diverse resource
requirements of application in a datacenter network, which can be represented
by the coflow abstraction. The conventional method of creating hand-crafted coflow
heuristics for admission or scheduling for different workloads is practically
infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based
coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level
performance objective, i.e., maximize successful coflow admissions, without manual
feature engineering. LCS is trained on a production trace, which has online coflow
arrivals. The evaluation results show that LCS is able to learn a reasonable admission
policy that admits more coflows than state-of-the-art Varys heuristic while meeting
their deadlines.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Learning Coflow Admissions. In: IEEE INFOCOM 2021 -
IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE
Communications Society. doi:10.1109/INFOCOMWKSHPS51825.2021.9484599'
apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Coflow Admissions. In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
Vancouver BC Canada: IEEE Communications Society. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599'
bibtex: '@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={10.1109/INFOCOMWKSHPS51825.2021.9484599},
booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif
and Karl, Holger} }'
chicago: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
IEEE Communications Society, n.d. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599.
ieee: A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver
BC Canada.
mla: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” IEEE INFOCOM
2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS),
IEEE Communications Society, doi:10.1109/INFOCOMWKSHPS51825.2021.9484599.
short: 'A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer
Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.'
conference:
end_date: 2021-05-13
location: Vancouver BC Canada
name: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
start_date: 2021-05-10
date_created: 2021-01-16T18:24:19Z
date_updated: 2022-01-06T06:54:42Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/INFOCOMWKSHPS51825.2021.9484599
keyword:
- Coflow scheduling
- Reinforcement learning
- Deadlines
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9484599
project:
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '4'
name: SFB 901 - Project Area C
- _id: '1'
name: SFB 901
publication: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)
publication_status: accepted
publisher: IEEE Communications Society
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9484599
status: public
title: Learning Coflow Admissions
type: conference
user_id: '63288'
year: '2021'
...
---
_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: '17082'
abstract:
- lang: eng
text: Data-parallel applications run on cluster of servers in a datacenter and their
communication triggers correlated resource demand on multiple links that can be
abstracted as coflow. They often desire predictable network performance, which
can be passed to network via coflow abstraction for application-aware network
scheduling. In this paper, we propose a heuristic and an optimization algorithm
for predictable network performance such that they guarantee coflows completion
within their deadlines. The algorithms also ensure high network utilization, i.e.,
it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms
via trace-driven simulation and show that they admit 1.1x more coflows than the
Varys scheme while meeting their deadlines.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Coflow Scheduling with Performance Guarantees for Data
Center Applications. In: 2020 20th IEEE/ACM International Symposium on Cluster,
Cloud and Internet Computing (CCGRID). IEEE Computer Society; 2020. doi:https://doi.org/10.1109/CCGrid49817.2020.00010'
apa: 'Hasnain, A., & Karl, H. (2020). Coflow Scheduling with Performance Guarantees
for Data Center Applications. In 2020 20th IEEE/ACM International Symposium
on Cluster, Cloud and Internet Computing (CCGRID). Melbourne, Australia: IEEE
Computer Society. https://doi.org/10.1109/CCGrid49817.2020.00010'
bibtex: '@inproceedings{Hasnain_Karl_2020, title={Coflow Scheduling with Performance
Guarantees for Data Center Applications}, DOI={https://doi.org/10.1109/CCGrid49817.2020.00010},
booktitle={2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet
Computing (CCGRID)}, publisher={IEEE Computer Society}, author={Hasnain, Asif
and Karl, Holger}, year={2020} }'
chicago: Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees
for Data Center Applications.” In 2020 20th IEEE/ACM International Symposium
on Cluster, Cloud and Internet Computing (CCGRID). IEEE Computer Society,
2020. https://doi.org/10.1109/CCGrid49817.2020.00010.
ieee: A. Hasnain and H. Karl, “Coflow Scheduling with Performance Guarantees for
Data Center Applications,” in 2020 20th IEEE/ACM International Symposium on
Cluster, Cloud and Internet Computing (CCGRID), Melbourne, Australia, 2020.
mla: Hasnain, Asif, and Holger Karl. “Coflow Scheduling with Performance Guarantees
for Data Center Applications.” 2020 20th IEEE/ACM International Symposium on
Cluster, Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020,
doi:https://doi.org/10.1109/CCGrid49817.2020.00010.
short: 'A. Hasnain, H. Karl, in: 2020 20th IEEE/ACM International Symposium on Cluster,
Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020.'
conference:
location: Melbourne, Australia
name: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet
Computing (CCGRID)
date_created: 2020-06-06T07:40:45Z
date_updated: 2022-01-06T06:53:04Z
ddc:
- '000'
department:
- _id: '75'
doi: https://doi.org/10.1109/CCGrid49817.2020.00010
keyword:
- Coflow
- Scheduling
- Deadlines
- Data centers
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/abstract/document/9139642
project:
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '1'
name: SFB 901
publication: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet
Computing (CCGRID)
publication_status: published
publisher: IEEE Computer Society
status: public
title: Coflow Scheduling with Performance Guarantees for Data Center Applications
type: conference
user_id: '63288'
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: '16278'
abstract:
- lang: eng
text: Currently, the coexistence of multiple users and devices challenges the network's
ability to reliably connect them. This article proposes a novel communication
architecture that satisfies the requirements of fifth-generation (5G) mobile network
applications. In particular, this architecture extends and combines ultra-dense
networking (UDN), multi-access edge computing (MEC), and virtual infrastructure
manager (VIM) concepts to provide a flexible network of moving radio access (RA)
nodes, flying or moving to areas where users and devices struggle for connectivity
and data rate. Furthermore, advances in radio communications and non-orthogonal
multiple access (NOMA), virtualization technologies and energy-awareness mechanisms
are integrated towards a mobile UDN that not only allows RA nodes to follow the
user but also enables the virtualized network functions (VNFs) to adapt to user
mobility by migrating from one node to another. Performance evaluation shows that
the underlying network improves connectivity of users and devices through the
flexible deployment of moving RA nodes and the use of NOMA.
article_number: '100250'
author:
- first_name: Nikolaos
full_name: Nomikos, Nikolaos
last_name: Nomikos
- first_name: Emmanouel T.
full_name: Michailidis, Emmanouel T.
last_name: Michailidis
- first_name: Panagiotis
full_name: Trakadas, Panagiotis
last_name: Trakadas
- first_name: Demosthenes
full_name: Vouyioukas, Demosthenes
last_name: Vouyioukas
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Josep
full_name: Martrat, Josep
last_name: Martrat
- first_name: Theodore
full_name: Zahariadis, Theodore
last_name: Zahariadis
- first_name: Konstantinos
full_name: Papadopoulos, Konstantinos
last_name: Papadopoulos
- first_name: Stamatis
full_name: Voliotis, Stamatis
last_name: Voliotis
citation:
ama: Nomikos N, Michailidis ET, Trakadas P, et al. A UAV-based moving 5G RAN for
massive connectivity of mobile users and IoT devices. Vehicular Communications.
2020. doi:10.1016/j.vehcom.2020.100250
apa: Nomikos, N., Michailidis, E. T., Trakadas, P., Vouyioukas, D., Karl, H., Martrat,
J., … Voliotis, S. (2020). A UAV-based moving 5G RAN for massive connectivity
of mobile users and IoT devices. Vehicular Communications. https://doi.org/10.1016/j.vehcom.2020.100250
bibtex: '@article{Nomikos_Michailidis_Trakadas_Vouyioukas_Karl_Martrat_Zahariadis_Papadopoulos_Voliotis_2020,
title={A UAV-based moving 5G RAN for massive connectivity of mobile users and
IoT devices}, DOI={10.1016/j.vehcom.2020.100250},
number={100250}, journal={Vehicular Communications}, author={Nomikos, Nikolaos
and Michailidis, Emmanouel T. and Trakadas, Panagiotis and Vouyioukas, Demosthenes
and Karl, Holger and Martrat, Josep and Zahariadis, Theodore and Papadopoulos,
Konstantinos and Voliotis, Stamatis}, year={2020} }'
chicago: Nomikos, Nikolaos, Emmanouel T. Michailidis, Panagiotis Trakadas, Demosthenes
Vouyioukas, Holger Karl, Josep Martrat, Theodore Zahariadis, Konstantinos Papadopoulos,
and Stamatis Voliotis. “A UAV-Based Moving 5G RAN for Massive Connectivity of
Mobile Users and IoT Devices.” Vehicular Communications, 2020. https://doi.org/10.1016/j.vehcom.2020.100250.
ieee: N. Nomikos et al., “A UAV-based moving 5G RAN for massive connectivity
of mobile users and IoT devices,” Vehicular Communications, 2020.
mla: Nomikos, Nikolaos, et al. “A UAV-Based Moving 5G RAN for Massive Connectivity
of Mobile Users and IoT Devices.” Vehicular Communications, 100250, 2020,
doi:10.1016/j.vehcom.2020.100250.
short: N. Nomikos, E.T. Michailidis, P. Trakadas, D. Vouyioukas, H. Karl, J. Martrat,
T. Zahariadis, K. Papadopoulos, S. Voliotis, Vehicular Communications (2020).
date_created: 2020-03-10T15:59:56Z
date_updated: 2022-01-06T06:52:48Z
department:
- _id: '75'
doi: 10.1016/j.vehcom.2020.100250
language:
- iso: eng
publication: Vehicular Communications
publication_identifier:
issn:
- 2214-2096
publication_status: published
status: public
title: A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT
devices
type: journal_article
user_id: '126'
year: '2020'
...
---
_id: '16280'
abstract:
- lang: eng
text: 'Assigning bands of the wireless spectrum as resources to users is a common
problem in wireless networks. Typically, frequency bands were assumed to be available
in a stable manner. Nevertheless, in recent scenarios where wireless networks
may be deployed in unknown environments, spectrum competition is considered, making
it uncertain whether a frequency band is available at all or at what quality.
To fully exploit such resources with uncertain availability, the multi-armed bandit
(MAB) method, a representative online learning technique, has been applied to
design spectrum scheduling algorithms. This article surveys such proposals. We
describe the following three aspects: how to model spectrum scheduling problems
within the MAB framework, what the main thread is following which prevalent algorithms
are designed, and how to evaluate algorithm performance and complexity. We also
give some promising directions for future research in related fields.'
author:
- first_name: Feng
full_name: Li, Feng
last_name: Li
- first_name: Dongxiao
full_name: Yu, Dongxiao
last_name: Yu
- first_name: Huan
full_name: Yang, Huan
last_name: Yang
- first_name: Jiguo
full_name: Yu, Jiguo
last_name: Yu
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Xiuzhen
full_name: Cheng, Xiuzhen
last_name: Cheng
citation:
ama: 'Li F, Yu D, Yang H, Yu J, Karl H, Cheng X. Multi-Armed-Bandit-Based Spectrum
Scheduling Algorithms in Wireless Networks: A Survey. IEEE Wireless Communications.
2020:24-30. doi:10.1109/mwc.001.1900280'
apa: 'Li, F., Yu, D., Yang, H., Yu, J., Karl, H., & Cheng, X. (2020). Multi-Armed-Bandit-Based
Spectrum Scheduling Algorithms in Wireless Networks: A Survey. IEEE Wireless
Communications, 24–30. https://doi.org/10.1109/mwc.001.1900280'
bibtex: '@article{Li_Yu_Yang_Yu_Karl_Cheng_2020, title={Multi-Armed-Bandit-Based
Spectrum Scheduling Algorithms in Wireless Networks: A Survey}, DOI={10.1109/mwc.001.1900280},
journal={IEEE Wireless Communications}, author={Li, Feng and Yu, Dongxiao and
Yang, Huan and Yu, Jiguo and Karl, Holger and Cheng, Xiuzhen}, year={2020}, pages={24–30}
}'
chicago: 'Li, Feng, Dongxiao Yu, Huan Yang, Jiguo Yu, Holger Karl, and Xiuzhen Cheng.
“Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks:
A Survey.” IEEE Wireless Communications, 2020, 24–30. https://doi.org/10.1109/mwc.001.1900280.'
ieee: 'F. Li, D. Yu, H. Yang, J. Yu, H. Karl, and X. Cheng, “Multi-Armed-Bandit-Based
Spectrum Scheduling Algorithms in Wireless Networks: A Survey,” IEEE Wireless
Communications, pp. 24–30, 2020.'
mla: 'Li, Feng, et al. “Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms
in Wireless Networks: A Survey.” IEEE Wireless Communications, 2020, pp.
24–30, doi:10.1109/mwc.001.1900280.'
short: F. Li, D. Yu, H. Yang, J. Yu, H. Karl, X. Cheng, IEEE Wireless Communications
(2020) 24–30.
date_created: 2020-03-10T16:02:30Z
date_updated: 2022-01-06T06:52:48Z
department:
- _id: '75'
doi: 10.1109/mwc.001.1900280
language:
- iso: eng
page: 24-30
publication: IEEE Wireless Communications
publication_identifier:
issn:
- 1536-1284
- 1558-0687
publication_status: published
status: public
title: 'Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks:
A Survey'
type: journal_article
user_id: '126'
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: '13770'
author:
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Karl H, Kundisch D, Meyer auf der Heide F, Wehrheim H. A Case for a New IT
Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering.
2020;62(6):467-481. doi:10.1007/s12599-019-00627-x'
apa: 'Karl, H., Kundisch, D., Meyer auf der Heide, F., & Wehrheim, H. (2020).
A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information
Systems Engineering, 62(6), 467–481. https://doi.org/10.1007/s12599-019-00627-x'
bibtex: '@article{Karl_Kundisch_Meyer auf der Heide_Wehrheim_2020, title={A Case
for a New IT Ecosystem: On-The-Fly Computing}, volume={62}, DOI={10.1007/s12599-019-00627-x},
number={6}, journal={Business & Information Systems Engineering}, publisher={Springer},
author={Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and
Wehrheim, Heike}, year={2020}, pages={467–481} }'
chicago: 'Karl, Holger, Dennis Kundisch, Friedhelm Meyer auf der Heide, and Heike
Wehrheim. “A Case for a New IT Ecosystem: On-The-Fly Computing.” Business &
Information Systems Engineering 62, no. 6 (2020): 467–81. https://doi.org/10.1007/s12599-019-00627-x.'
ieee: 'H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for
a New IT Ecosystem: On-The-Fly Computing,” Business & Information Systems
Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi: 10.1007/s12599-019-00627-x.'
mla: 'Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.”
Business & Information Systems Engineering, vol. 62, no. 6, Springer,
2020, pp. 467–81, doi:10.1007/s12599-019-00627-x.'
short: H. Karl, D. Kundisch, F. Meyer auf der Heide, H. Wehrheim, Business &
Information Systems Engineering 62 (2020) 467–481.
date_created: 2019-10-10T13:41:06Z
date_updated: 2022-12-02T09:27:17Z
ddc:
- '004'
department:
- _id: '276'
- _id: '75'
- _id: '63'
- _id: '77'
doi: 10.1007/s12599-019-00627-x
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2019-12-12T10:24:47Z
date_updated: 2019-12-12T10:24:47Z
file_id: '15311'
file_name: Karl2019_Article_ACaseForANewITEcosystemOn-The-.pdf
file_size: 454532
relation: main_file
success: 1
file_date_updated: 2019-12-12T10:24:47Z
has_accepted_license: '1'
intvolume: ' 62'
issue: '6'
language:
- iso: eng
page: 467-481
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '3'
name: SFB 901 - Project Area B
- _id: '4'
name: SFB 901 - Project Area C
- _id: '82'
name: SFB 901 - Project Area T
- _id: '5'
name: SFB 901 - Subproject A1
- _id: '6'
name: SFB 901 - Subproject A2
- _id: '7'
name: SFB 901 - Subproject A3
- _id: '8'
name: SFB 901 - Subproject A4
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '13'
name: SFB 901 - Subproject C1
- _id: '14'
name: SFB 901 - Subproject C2
- _id: '15'
name: SFB 901 - Subproject C3
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '17'
name: SFB 901 - Subproject C5
- _id: '83'
name: SFB 901 -Subproject T1
- _id: '84'
name: SFB 901 -Subproject T2
- _id: '107'
name: SFB 901 -Subproject T3
- _id: '158'
name: 'SFB 901 - T4: SFB 901 -Subproject T4'
publication: Business & Information Systems Engineering
publication_status: published
publisher: Springer
status: public
title: 'A Case for a New IT Ecosystem: On-The-Fly Computing'
type: journal_article
user_id: '477'
volume: 62
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'
...