Smart City Gnosys

Smart city article details

Title	Workload-Based Adaptive Decision-Making For Edge Server Layout With Deep Reinforcement Learning
ID_Doc	62053
Authors	Li S.; Zhou Y.; Zhou B.; Wang Z.
Year	2025
Published	Engineering Applications of Artificial Intelligence, 139
DOI	http://dx.doi.org/10.1016/j.engappai.2024.109662
Abstract	Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%. © 2024 Elsevier Ltd
Author Keywords	Deep reinforcement learning; Mobile edge computing; Server placement

Similar Articles

Id	Similarity	Authors	Title	Published
40838	0.883	Liu L.; Xu Z.	Optimizing Lightweight Neural Networks For Efficient Mobile Edge Computing	Scientific Reports, 15, 1 (2025)
18054	0.873	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)
46071	0.872	Cui X.	Resource Allocation In Iot Edge Computing Networks Based On Reinforcement Learning	Advances in Transdisciplinary Engineering, 70 (2025)
20631	0.87	Li H.; Wang G.; Liu Y.; Ren S.; Li T.; Wang D.	Distributed Edge Computing Resource Allocation Algorithm Based On Drl In Lte Access Network	4th International Conference on Smart Grid and Smart Cities, ICSGSC 2020 (2020)
21063	0.869	He B.; Li H.; Chen T.	Drl-Based Computing Offloading Approach For Large-Scale Heterogeneous Tasks In Mobile Edge Computing	Concurrency and Computation: Practice and Experience, 36, 19 (2024)
38090	0.869	Jiao T.; Feng X.; Guo C.; Wang D.; Song J.	Multi-Agent Deep Reinforcement Learning For Efficient Computation Offloading In Mobile Edge Computing	Computers, Materials and Continua, 76, 3 (2023)
34289	0.869	Khdr S.O.; Azizi S.; Hassan H.O.	Iterative Weighted Randomized Algorithm For Edge Server Deployment In Mobile Edge Computing	Passer Journal of Basic and Applied Sciences, 7, 1 (2025)
38588	0.867	Asghari A.; Sohrabi M.K.	Multiobjective Edge Server Placement In Mobile-Edge Computing Using A Combination Of Multiagent Deep Q-Network And Coral Reefs Optimization	IEEE Internet of Things Journal, 9, 18 (2022)
18051	0.865	Agbaje P.; Nwafor E.; Olufowobi H.	Deep Reinforcement Learning For Energy-Efficient Task Offloading In Cooperative Vehicular Edge Networks	IEEE International Conference on Industrial Informatics (INDIN), 2023-July (2023)
40621	0.865	Hassan M.T.; Hosain M.K.	Optimization Of Computation Offloading In Mobile-Edge Computing Networks With Deep Reinforcement Approach	2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024 (2024)