TY - GEN
T1 - Definition of digital twin network data model in the context of edge-cloud continuum
AU - Raza, Syed Mohsan
AU - Minerva, Roberto
AU - Crespi, Noel
AU - Karech, Mehdi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - The telecommunications sector is devoting an initial interest in the representation of complex networks as Digital Twins. The concept of a Digital Twin Network (DTN) is a research topic, but it promises to be an important step for harmonizing different models of the Edge-Cloud Continuum. The DTN software framework aims at helping network operations by providing updated and complete views on the network or parts of it, and it also introduces the possibility to simulate the network behavior or to learn from network events history (Machine Learning) without jeopardizing the actual operations of resources. In addition, thanks to the representation capabilities of the DT, its usage in the network promises to support different stakeholders' views on their virtualized and physical infrastructure. This work tries to consolidate a DTN data model representing the elements of the Edge-Cloud Continuum by providing a layered (horizontal) and segmented (vertical) view of the infrastructure to all the involved stakeholders. The DTN model is an ontology where the linked classes represent properties and relations of networked components. This work aims to design a flexible and extensible ontology that describes the Edge-Cloud continuum usable in the telecommunications as well in the Cloud (IT and web) industries creating a bridge between the two.
AB - The telecommunications sector is devoting an initial interest in the representation of complex networks as Digital Twins. The concept of a Digital Twin Network (DTN) is a research topic, but it promises to be an important step for harmonizing different models of the Edge-Cloud Continuum. The DTN software framework aims at helping network operations by providing updated and complete views on the network or parts of it, and it also introduces the possibility to simulate the network behavior or to learn from network events history (Machine Learning) without jeopardizing the actual operations of resources. In addition, thanks to the representation capabilities of the DT, its usage in the network promises to support different stakeholders' views on their virtualized and physical infrastructure. This work tries to consolidate a DTN data model representing the elements of the Edge-Cloud Continuum by providing a layered (horizontal) and segmented (vertical) view of the infrastructure to all the involved stakeholders. The DTN model is an ontology where the linked classes represent properties and relations of networked components. This work aims to design a flexible and extensible ontology that describes the Edge-Cloud continuum usable in the telecommunications as well in the Cloud (IT and web) industries creating a bridge between the two.
KW - Data Model
KW - Digital Twin Network
KW - Edge-Cloud Continuum
KW - Ontology
KW - Service User
UR - https://www.scopus.com/pages/publications/85166487240
U2 - 10.1109/NetSoft57336.2023.10175444
DO - 10.1109/NetSoft57336.2023.10175444
M3 - Conference contribution
AN - SCOPUS:85166487240
T3 - 2023 IEEE 9th International Conference on Network Softwarization: Boosting Future Networks through Advanced Softwarization, NetSoft 2023 - Proceedings
SP - 402
EP - 407
BT - 2023 IEEE 9th International Conference on Network Softwarization
A2 - Bernardos, Carlos J.
A2 - Martini, Barbara
A2 - Rojas, Elisa
A2 - Verdi, Fabio Luciano
A2 - Zhu, Zuqing
A2 - Oki, Eiji
A2 - Parzyjegla, Helge
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Network Softwarization, NetSoft 2023
Y2 - 19 June 2023 through 23 June 2023
ER -