Smart City Gnosys

Smart city article details

Title	On Real-Time Scheduling In Fog Computing: A Reinforcement Learning Algorithm With Application To Smart Cities
ID_Doc	39777
Authors	Mattia G.P.; Beraldi R.
Year	2022
Published	2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022
DOI	http://dx.doi.org/10.1109/PerComWorkshops53856.2022.9767498
Abstract	Fog Computing is today a wide used paradigm that allows to distribute the computation in a geographic area. This not only makes possible to implement time-critical applications but opens the study to a series of solutions which permit to smartly organise the traffic among a set of Fog nodes, which constitute the core of the Fog Computing paradigm. A typical smart city setting is subject to a continuous change of traffic conditions, a node that was saturated can become almost completely unloaded and this creates the need of designing an algorithm which allows to meet the strict deadlines of the tasks but at the same time it can choose the best scheduling policy according to the current load situation that can vary at any time. In this paper, we use a Reinforcement Learning approach to design such an algorithm starting from the power-of-random choice paradigm, used as a baseline. By showing results from our delay-based simulator, we demonstrate how such distributed reinforcement learning approach is able to maximise the rate of the tasks executed within the deadline in a way that is equal to every node, both in a fixed load condition and in a real geographic scenario. © 2022 IEEE.
Author Keywords	fog computing; real-time; reinforcement learning; scheduling; smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
40037	0.963	Proietti Mattia G.; Beraldi R.	Online Decentralized Scheduling In Fog Computing For Smart Cities Based On Reinforcement Learning	IEEE Transactions on Cognitive Communications and Networking, 10, 4 (2024)
26786	0.913	Al-Hashimi M.A.A.; Rahiman A.R.; Muhammed A.; Hamid N.A.W.	Fog-Cloud Scheduling Simulator For Reinforcement Learning Algorithms	International Journal of Information Technology (Singapore), 17, 5 (2025)
26075	0.91	Nagabushnam G.; Kim K.H.	Faddeer: A Deep Multi-Agent Reinforcement Learning-Based Scheduling Algorithm For Aperiodic Tasks In Heterogeneous Fog Computing Networks	Cluster Computing, 28, 6 (2025)
35418	0.899	Alorbani A.; Bauer M.	Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning	2021 IEEE International Smart Cities Conference, ISC2 2021 (2021)
21373	0.886	Abdelghany H.M.	Dynamic Resource Management And Task Offloading Framework For Fog Computing	Journal of Grid Computing, 23, 2 (2025)
36528	0.881	Shruthi G.; Mundada M.R.; Sowmya B.J.; Supreeth S.	Mayfly Taylor Optimisation-Based Scheduling Algorithm With Deep Reinforcement Learning For Dynamic Scheduling In Fog-Cloud Computing	Applied Computational Intelligence and Soft Computing, 2022 (2022)
15454	0.879	Peng K.; Liu P.; Xu X.; Zhou X.	Computing Offloading Of Multi-Dependent Tasks In Smart Cities; [面向智慧城市的多依赖任务计算迁移研究]	Yingyong Kexue Xuebao/Journal of Applied Sciences, 41, 3 (2023)
26755	0.876	Alsamman M.; Fazea Y.; Mohammed F.; Kehail M.A.M.	Fog Computing In Smart Cities: A Systematic Review	2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023 (2023)
1711	0.874	Canali C.; Lancellotti R.	A Fog Computing Service Placement For Smart Cities Based On Genetic Algorithms	CLOSER 2019 - Proceedings of the 9th International Conference on Cloud Computing and Services Science (2019)
40898	0.874	Negi V.; Joshi D.; Sharma A.	Optimizing Task Allocation In Fog-Based Iot For Smart City Solutions	Citizen-Centric Artificial Intelligence for Smart Cities (2025)