Smart City Gnosys

Smart city article details

Title	A Distributed Load Balancing Method For Iot/Fog/Cloud Environments With Volatile Resource Support
ID_Doc	1522
Authors	Shamsa Z.; Rezaee A.; Adabi S.; Rahimabadi A.M.; Rahmani A.M.
Year	2024
Published	Cluster Computing, 27, 4
DOI	http://dx.doi.org/10.1007/s10586-024-04403-9
Abstract	In cloud/fog-based environments, resource management is an important and challenging process. The deadline-based workflow scheduling mechanism is a common practice in such systems to overcome the complexities of resource management. However, many proposed approaches suffer from resource overloading/underloading, ignoring volunteer and volatile resources, and acting reactively. This paper presents a load-balancing method for IoT/Fog/Cloud environments integrated with local schedulers based on predicting workload and the presence of volatile mobile nodes (as dynamic resources). The proposed approach, firstly, turns the environment into a grid of equal-sized cells to reduce the system’s complexity. Then, the overall status of intra-cell resources (overloaded, underloaded, or normal) is estimated. This estimation is done according to the workload prediction and available dynamic resources. Finally, an exhaustive search is applied to dispatch extra workflows from an overloaded cell to an underloaded one in such a way as to avoid missing workflow deadlines and improve system performance. The proposed method is intended to be scalable and decentralized by nature, allowing it to be used in large-scale settings such as smart cities. Extensive software simulation is used to evaluate and compare the proposed method to with two recently published works. The simulation results show that the proposed method outperforms others regarding job completion rate, workload variances, and time-related parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	Cloud computing; Fog computing; Internet of things (IoT); Load balancing; Long-short term memory

Similar Articles

Id	Similarity	Authors	Title	Published
4114	0.91	Mahdi R.M.; Hassan H.J.; Abdulsaheb G.M.	A Review Load Balancing Algorithms In Fog Computing	BIO Web of Conferences, 97 (2024)
1722	0.899	Singh P.; Kaur R.; Rashid J.; Juneja S.; Dhiman G.; Kim J.; Ouaissa M.	A Fog-Cluster Based Load-Balancing Technique	Sustainability (Switzerland), 14, 13 (2022)
20661	0.896	Beraldi R.; Canali C.; Lancellotti R.; Mattia G.P.	Distributed Load Balancing For Heterogeneous Fog Computing Infrastructures In Smart Cities	Pervasive and Mobile Computing, 67 (2020)
40664	0.875	Prasad C.R.; Sandeep Kumar V.; Rao P.R.; Kollem S.; Yalabaka S.; Samala S.	Optimization Of Task Offloading For Smart Cities Using Iot With Fog Computing- A Survey	2022 International Conference on Signal and Information Processing, IConSIP 2022 (2022)
3954	0.874	Beraldi R.; Canali C.; Lancellotti R.; Mattia G.P.	A Random Walk Based Load Balancing Algorithm For Fog Computing	2020 5th International Conference on Fog and Mobile Edge Computing, FMEC 2020 (2020)
46065	0.874	Jamil B.; Ijaz H.; Shojafar M.; Munir K.; Buyya R.	Resource Allocation And Task Scheduling In Fog Computing And Internet Of Everything Environments: A Taxonomy, Review, And Future Directions	ACM Computing Surveys, 54, 11s (2022)
2203	0.872	Reddy K.H.K.; Srivastava G.; Goswami R.S.; Roy D.S.	A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling For Unified Iot Applications In Smart Cities	IEEE Transactions on Network and Service Management, 21, 2 (2024)
4182	0.871	Kumar S.; Singh P.; Singh A.	A Review Of Optimized Computational Strategies For Iot: Cloud, Fog, And Edge Computing Approaches	Proceedings of 5th International Conference on Pervasive Computing and Social Networking, ICPCSN 2025 (2025)
7771	0.866	Nithiyanandam; Velvizhi R.; Priyadharshini S.P.; Poongavanam N.	An Effective Method For Distributing Workloads In Smart City Using Sensor Networks Enabled By Fog	IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024 - Proceedings (2024)
7894	0.862	Puttaswamy N.G.; Murthy A.N.	An Efficient Reconfigurable Workload Balancing Scheme For Fog Computing Network Using Internet Of Things Devices	International Journal of Electrical and Computer Engineering, 13, 6 (2023)