---
_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: '25278'
abstract:
- lang: eng
text: Using Service Function Chaining (SFC) in wireless networks became popular
in many domains like networking and multimedia. It relies on allocating network
resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm,
so that it optimizes the performance of the SFC. When the load of incoming requests
-- competing for the limited network resources -- increases, it becomes challenging
to decide which requests should be admitted and which one should be rejected.
In this work, we propose a deep Reinforcement learning (RL) solution that can
learn the admission policy for different dependencies, such as the service lifetime
and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve
baseline that admits a request whenever there are available resources. We show
that deep RL outperforms the baseline and provides higher acceptance rate with
low rejections even when there are enough resources.
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Fabian Jakob
full_name: Sauer, Fabian Jakob
last_name: Sauer
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Sauer FJ, Karl H. Reinforcement Learning for Admission Control in
Wireless Virtual Network Embedding. In: 2021 IEEE International Conference
on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21). ; 2021.'
apa: Afifi, H., Sauer, F. J., & Karl, H. (2021). Reinforcement Learning for
Admission Control in Wireless Virtual Network Embedding. 2021 IEEE International
Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21).
bibtex: '@inproceedings{Afifi_Sauer_Karl_2021, place={Hyderabad, India}, title={Reinforcement
Learning for Admission Control in Wireless Virtual Network Embedding}, booktitle={2021
IEEE International Conference on Advanced Networks and Telecommunications Systems
(ANTS) (ANTS’21)}, author={Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger},
year={2021} }'
chicago: Afifi, Haitham, Fabian Jakob Sauer, and Holger Karl. “Reinforcement Learning
for Admission Control in Wireless Virtual Network Embedding.” In 2021 IEEE
International Conference on Advanced Networks and Telecommunications Systems (ANTS)
(ANTS’21). Hyderabad, India, 2021.
ieee: H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission
Control in Wireless Virtual Network Embedding,” 2021.
mla: Afifi, Haitham, et al. “Reinforcement Learning for Admission Control in Wireless
Virtual Network Embedding.” 2021 IEEE International Conference on Advanced
Networks and Telecommunications Systems (ANTS) (ANTS’21), 2021.
short: 'H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on
Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad,
India, 2021.'
date_created: 2021-10-04T10:42:20Z
date_updated: 2022-01-06T06:56:58Z
ddc:
- '000'
file:
- access_level: closed
content_type: application/pdf
creator: hafifi
date_created: 2021-10-04T10:43:19Z
date_updated: 2021-10-04T10:43:19Z
file_id: '25279'
file_name: Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf
file_size: 534737
relation: main_file
success: 1
file_date_updated: 2021-10-04T10:43:19Z
has_accepted_license: '1'
keyword:
- reinforcement learning
- admission control
- wireless sensor networks
language:
- iso: eng
place: Hyderabad, India
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 2021 IEEE International Conference on Advanced Networks and Telecommunications
Systems (ANTS) (ANTS'21)
status: public
title: Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
type: conference
user_id: '65718'
year: '2021'
...
---
_id: '25281'
abstract:
- lang: eng
text: "Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal
processing applications. Due to the spatial diversity of the microphone and their
relative position to the acoustic source, not all microphones are equally useful
for subsequent audio signal processing tasks, nor do they all have the same wireless
data transmission rates. Hence, a central task in WASNs is to balance a microphone’s
estimated acoustic utility against its transmission delay, selecting a best-possible
subset of microphones to record audio signals.\r\n\r\nIn this work, we use reinforcement
learning to decide if a microphone should be used or switched off to maximize
the acoustic quality at low transmission delays, while minimizing switching frequency.
In experiments with moving sources in a simulated acoustic environment, our method
outperforms naive baseline comparisons"
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Michael
full_name: Guenther, Michael
last_name: Guenther
- first_name: Andreas
full_name: Brendel, Andreas
last_name: Brendel
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
citation:
ama: 'Afifi H, Guenther M, Brendel A, Karl H, Kellermann W. Reinforcement Learning-based
Microphone Selection in Wireless Acoustic Sensor Networks considering Network
and Acoustic Utilities. In: 14. ITG Conference on Speech Communication (ITG
2021). ; 2021.'
apa: Afifi, H., Guenther, M., Brendel, A., Karl, H., & Kellermann, W. (2021).
Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
Networks considering Network and Acoustic Utilities. 14. ITG Conference on
Speech Communication (ITG 2021).
bibtex: '@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, title={Reinforcement
Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
Network and Acoustic Utilities}, booktitle={14. ITG Conference on Speech Communication
(ITG 2021)}, author={Afifi, Haitham and Guenther, Michael and Brendel, Andreas
and Karl, Holger and Kellermann, Walter}, year={2021} }'
chicago: Afifi, Haitham, Michael Guenther, Andreas Brendel, Holger Karl, and Walter
Kellermann. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic
Sensor Networks Considering Network and Acoustic Utilities.” In 14. ITG Conference
on Speech Communication (ITG 2021), 2021.
ieee: H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement
Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
Network and Acoustic Utilities,” 2021.
mla: Afifi, Haitham, et al. “Reinforcement Learning-Based Microphone Selection in
Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.”
14. ITG Conference on Speech Communication (ITG 2021), 2021.
short: 'H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference
on Speech Communication (ITG 2021), 2021.'
date_created: 2021-10-04T10:59:50Z
date_updated: 2022-01-06T06:56:59Z
ddc:
- '620'
file:
- access_level: closed
content_type: application/pdf
creator: hafifi
date_created: 2021-10-04T10:58:07Z
date_updated: 2021-10-04T10:58:07Z
file_id: '25282'
file_name: ITG_2021_paper_26 (3).pdf
file_size: 283616
relation: main_file
success: 1
file_date_updated: 2021-10-04T10:58:07Z
has_accepted_license: '1'
keyword:
- microphone utility
- microphone selection
- wireless acoustic sensor network
- network delay
- reinforcement learning
language:
- iso: eng
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 14. ITG Conference on Speech Communication (ITG 2021)
status: public
title: Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
Networks considering Network and Acoustic Utilities
type: conference
user_id: '65718'
year: '2021'
...
---
_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: '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: '21479'
abstract:
- lang: eng
text: Two of the most important metrics when developing Wireless Sensor Networks
(WSNs) applications are the Quality of Information (QoI) and Quality of Service
(QoS). The former is used to specify the quality of the collected data by the
sensors (e.g., measurements error or signal's intensity), while the latter defines
the network's performance and availability (e.g., packet losses and latency).
In this paper, we consider an example of wireless acoustic sensor networks, where
we select a subset of microphones for two different objectives. First, we maximize
the recording quality under QoS constraints. Second, we apply a trade-off between
QoI and QoS. We formulate the problem as a constrained Markov Decision Problem
(MDP) and solve it using reinforcement learning (RL). We compare the RL solution
to a baseline model and show that in case of QoS-guarantee objective, the RL solution
has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the
baseline with improvements up to 23\%, when using the trade-off objective.
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Ramaswamy A, Karl H. A Reinforcement Learning QoI/QoS-Aware Approach
in Acoustic Sensor Networks. In: 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021). ; 2021.'
apa: Afifi, H., Ramaswamy, A., & Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks. In 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021).
bibtex: '@inproceedings{Afifi_Ramaswamy_Karl_2021, title={A Reinforcement Learning
QoI/QoS-Aware Approach in Acoustic Sensor Networks}, booktitle={2021 IEEE 18th
Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021)},
author={Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, year={2021}
}'
chicago: Afifi, Haitham, Arunselvan Ramaswamy, and Holger Karl. “A Reinforcement
Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks.” In 2021 IEEE
18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC
2021), 2021.
ieee: H. Afifi, A. Ramaswamy, and H. Karl, “A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks,” in 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021), 2021.
mla: Afifi, Haitham, et al. “A Reinforcement Learning QoI/QoS-Aware Approach in
Acoustic Sensor Networks.” 2021 IEEE 18th Annual Consumer Communications \&
Networking Conference (CCNC) (CCNC 2021), 2021.
short: 'H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021), 2021.'
date_created: 2021-03-12T16:03:53Z
date_updated: 2022-01-06T06:55:00Z
keyword:
- reinforcement learning
- wireless sensor networks
- resource allocation
- acoustic sensor networks
language:
- iso: eng
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 2021 IEEE 18th Annual Consumer Communications \& Networking Conference
(CCNC) (CCNC 2021)
status: public
title: A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks
type: conference
user_id: '65718'
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: '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: '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: '13443'
abstract:
- lang: eng
text: "This work considers the problem of control and resource allocation in networked\r\nsystems.
To this end, we present DIRA a Deep reinforcement learning based Iterative Resource\r\nAllocation
algorithm, which is scalable and control-aware. Our algorithm is tailored towards\r\nlarge-scale
problems where control and scheduling need to act jointly to optimize performance.\r\nDIRA
can be used to schedule general time-domain optimization based controllers. In
the present\r\nwork, we focus on control designs based on suitably adapted linear
quadratic regulators. We\r\napply our algorithm to networked systems with correlated
fading communication channels. Our\r\nsimulations show that DIRA scales well to
large scheduling problems."
author:
- first_name: Adrian
full_name: Redder, Adrian
id: '52265'
last_name: Redder
orcid: https://orcid.org/0000-0001-7391-4688
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Daniel
full_name: Quevedo, Daniel
last_name: Quevedo
citation:
ama: 'Redder A, Ramaswamy A, Quevedo D. Deep reinforcement learning for scheduling
in large-scale networked control systems. In: Proceedings of the 8th IFAC Workshop
on Distributed Estimation and Control in Networked Systems. ; 2019.'
apa: Redder, A., Ramaswamy, A., & Quevedo, D. (2019). Deep reinforcement learning
for scheduling in large-scale networked control systems. In Proceedings of
the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems.
Chicago, USA.
bibtex: '@inproceedings{Redder_Ramaswamy_Quevedo_2019, title={Deep reinforcement
learning for scheduling in large-scale networked control systems}, booktitle={Proceedings
of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems},
author={Redder, Adrian and Ramaswamy, Arunselvan and Quevedo, Daniel}, year={2019}
}'
chicago: Redder, Adrian, Arunselvan Ramaswamy, and Daniel Quevedo. “Deep Reinforcement
Learning for Scheduling in Large-Scale Networked Control Systems.” In Proceedings
of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems,
2019.
ieee: A. Redder, A. Ramaswamy, and D. Quevedo, “Deep reinforcement learning for
scheduling in large-scale networked control systems,” in Proceedings of the
8th IFAC Workshop on Distributed Estimation and Control in Networked Systems,
Chicago, USA, 2019.
mla: Redder, Adrian, et al. “Deep Reinforcement Learning for Scheduling in Large-Scale
Networked Control Systems.” Proceedings of the 8th IFAC Workshop on Distributed
Estimation and Control in Networked Systems, 2019.
short: 'A. Redder, A. Ramaswamy, D. Quevedo, in: Proceedings of the 8th IFAC Workshop
on Distributed Estimation and Control in Networked Systems, 2019.'
conference:
end_date: 2019-09-17
location: Chicago, USA
name: 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems
- NECSYS 2019
start_date: 2019-09-16
date_created: 2019-09-23T16:00:58Z
date_updated: 2022-01-06T06:51:36Z
ddc:
- '620'
file:
- access_level: local
content_type: application/pdf
creator: aredder
date_created: 2019-09-23T15:48:33Z
date_updated: 2019-09-23T16:21:16Z
file_id: '13444'
file_name: ifacconf.pdf
file_size: 371429
relation: main_file
file_date_updated: 2019-09-23T16:21:16Z
has_accepted_license: '1'
keyword:
- Networked control systems
- deep reinforcement learning
- large-scale systems
- resource scheduling
- stochastic control
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1905.05992
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control
in Networked Systems
publication_status: published
status: public
title: Deep reinforcement learning for scheduling in large-scale networked control
systems
type: conference
user_id: '52265'
year: '2019'
...