Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes

Alessandro Spallina; Andrea Araldo; Tijani Chahed; Hind Castel-Taleb; Antonella Di Stefano; Tulin Atmaca

doi:10.1109/NOMS54207.2022.9789942

Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes

Alessandro Spallina
, Andrea Araldo
, Tijani Chahed
, Hind Castel-Taleb
, Antonella Di Stefano
, Tulin Atmaca

Institut Polytechnique de Paris
Università degli Studi di Catania

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.

langue originale	Anglais
titre	Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022
Sous-titre	Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022
rédacteurs en chef	Pal Varga, Lisandro Zambenedetti Granville, Alex Galis, Istvan Godor, Noura Limam, Prosper Chemouil, Jerome Francois, Marc-Oliver Pahl
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9781665406017
Les DOIs	https://doi.org/10.1109/NOMS54207.2022.9789942
état	Publié - 1 janv. 2022
Evénement	2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 - Budapest, Hongrie Durée: 25 avr. 2022 → 29 avr. 2022

Série de publications

Nom	Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022

Une conférence

Une conférence	2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Pays/Territoire	Hongrie
La ville	Budapest
période	25/04/22 → 29/04/22

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre

Accès au document

10.1109/NOMS54207.2022.9789942

Contient cette citation

Spallina, A., Araldo, A., Chahed, T., Castel-Taleb, H., Di Stefano, A., & Atmaca, T. (2022). Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes. Dans P. Varga, L. Z. Granville, A. Galis, I. Godor, N. Limam, P. Chemouil, J. Francois, & M.-O. Pahl (eds.), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022 (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS54207.2022.9789942

Spallina, Alessandro ; Araldo, Andrea ; Chahed, Tijani et al. / Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes. Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Editeur / Pal Varga ; Lisandro Zambenedetti Granville ; Alex Galis ; Istvan Godor ; Noura Limam ; Prosper Chemouil ; Jerome Francois ; Marc-Oliver Pahl. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022).

@inproceedings{0cf5cd4a46bd4526bec8cefda1ed8d74,

title = "Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes",

abstract = "We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.",

keywords = "Edge Computing, Energy-efficiency, Markov Decision Processes, Multi-tenant Systems, Resource Allocation",

author = "Alessandro Spallina and Andrea Araldo and Tijani Chahed and Hind Castel-Taleb and \{Di Stefano\}, Antonella and Tulin Atmaca",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 ; Conference date: 25-04-2022 Through 29-04-2022",

year = "2022",

month = jan,

day = "1",

doi = "10.1109/NOMS54207.2022.9789942",

language = "English",

series = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Pal Varga and Granville, \{Lisandro Zambenedetti\} and Alex Galis and Istvan Godor and Noura Limam and Prosper Chemouil and Jerome Francois and Marc-Oliver Pahl",

booktitle = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022",

}

Spallina, A, Araldo, A , Chahed, T, Castel-Taleb, H, Di Stefano, A & Atmaca, T 2022, Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes. Dans P Varga, LZ Granville, A Galis, I Godor, N Limam, P Chemouil, J Francois & M-O Pahl (eds), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022, Budapest, Hongrie, 25/04/22. https://doi.org/10.1109/NOMS54207.2022.9789942

Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes. / Spallina, Alessandro; Araldo, Andrea ; Chahed, Tijani et al.
Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Ed. / Pal Varga; Lisandro Zambenedetti Granville; Alex Galis; Istvan Godor; Noura Limam; Prosper Chemouil; Jerome Francois; Marc-Oliver Pahl. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes

AU - Spallina, Alessandro

AU - Araldo, Andrea

AU - Chahed, Tijani

AU - Castel-Taleb, Hind

AU - Di Stefano, Antonella

AU - Atmaca, Tulin

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.

AB - We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.

KW - Edge Computing

KW - Energy-efficiency

KW - Markov Decision Processes

KW - Multi-tenant Systems

KW - Resource Allocation

U2 - 10.1109/NOMS54207.2022.9789942

DO - 10.1109/NOMS54207.2022.9789942

M3 - Conference contribution

AN - SCOPUS:85133190078

T3 - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022

BT - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022

A2 - Varga, Pal

A2 - Granville, Lisandro Zambenedetti

A2 - Galis, Alex

A2 - Godor, Istvan

A2 - Limam, Noura

A2 - Chemouil, Prosper

A2 - Francois, Jerome

A2 - Pahl, Marc-Oliver

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022

Y2 - 25 April 2022 through 29 April 2022

ER -

Spallina A, Araldo A , Chahed T, Castel-Taleb H, Di Stefano A, Atmaca T. Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes. Dans Varga P, Granville LZ, Galis A, Godor I, Limam N, Chemouil P, Francois J, Pahl MO, rédacteurs en chef, Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022). doi: 10.1109/NOMS54207.2022.9789942

Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation