Smart City Gnosys

Smart city article details

Title	Cloud-Edge Resource Management And Migration: Leveraging Online Learning For Digital Twin Re-Placement
ID_Doc	14480
Authors	Ranjbaran S.; Amadeo M.; Marche C.; Ruggeri G.; Sinha A.; Nitti M.
Year	2024
Published	2024 IEEE 10th World Forum on Internet of Things, WF-IoT 2024
DOI	http://dx.doi.org/10.1109/WF-IoT62078.2024.10811458
Abstract	The increasing demands of devices and services, particularly within the Internet of Everything (IoE), are driving the need for innovative solutions to manage the vast amounts of associated data. Initially used in manufacturing, Digital Twin (DT) technology is now recognized as essential in the 6 G ecosystem, supporting applications like smart cities and autonomous transportation, which benefit from the network's ultr-alow latency and high reliability. However, even if the literature faces the development and management of the DTs, it lacks comprehensive strategies for their deployment and placement in the networks. Current research mainly explores bringing DTs closer to devices through edge frameworks, without addressing dynamic resource management. In this sense, this paper proposes an online-learning-based model for deploying DTs at the edge to meet stringent latency requirements. The conceived approach leverages communication between two entities, a Cloud and an Edge Managers, ensuring optimal DT placement and efficient resource use. A performance evaluation shows the benefits of the conceived solution in terms of convergence time and latency compared to the most used centralized approaches. © 2024 IEEE.
Author Keywords	Cloud-Edge Migration; Digital Twin; DT Placement; Online Learning

Similar Articles

Id	Similarity	Authors	Title	Published
10281	0.904	Linkevics G.; Mosans G.; Kampars J.; Gulbe R.	Architectural Requirements For Digital Twin Development Platform: A Review Of Scientific Literature And Practical Considerations	2025 IEEE 12th Workshop on Advances in Information, Electronic and Electrical Engineering, AIEEE 2025 - Proceedings (2025)
20315	0.891	Shafik W.	Digital Twins Tools And Technologies	Digital Twins for Smart Cities and Villages (2024)
52672	0.879	Hakiri A.; Ben Yahia S.; Gokhale A.S.; Mellouli N.	Special Issue On Digital Twin For Future Networks And Emerging Iot Applications (Dt4Iot)	Future Generation Computer Systems, 161 (2024)
1481	0.878	Bozkaya E.	A Digital Twin Framework For Edge Server Placement In Mobile Edge Computing	4th International Informatics and Software Engineering Conference - Symposium Program, IISEC 2023 (2023)
18054	0.875	Zhang Y.; Wang L.; Liang W.	Deep Reinforcement Learning For Mobility-Aware Digital Twin Migrations In Edge Computing	IEEE Transactions on Services Computing, 18, 2 (2025)
20183	0.874	Ouedraogo E.B.; Hawbani A.; Wang X.; Liu Z.; Zhao L.; Al-Qaness M.A.A.; Alsamhi S.H.	Digital Twin Data Management: A Comprehensive Review	IEEE Transactions on Big Data (2025)
27846	0.873	Savaglio C.; Barbuto V.; Mangione F.; Fortino G.	Generative Digital Twins: A Novel Approach In The Iot Edge-Cloud Continuum	IEEE Internet of Things Magazine, 8, 1 (2025)
38627	0.872	Liang X.; Liang W.; Xu Z.; Zhang Y.; Jia X.	Multiple Service Model Refreshments In Digital Twin-Empowered Edge Computing	IEEE Transactions on Services Computing, 17, 5 (2024)
20316	0.869	Klar R.; Arvidsson N.; Angelakis V.	Digital Twins' Maturity: The Need For Interoperability	IEEE Systems Journal, 18, 1 (2024)
9383	0.869	Rinaldi S.; Iacono S.D.; Bellagente P.; Pasetti M.	Analysis Of Time Synchronization Challenges In Digital Twins For Edge-Enabled Data Centers In Smart Cities Scenario	Proceedings - 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025 (2025)