Smart City Gnosys
Smart city article details
| Title | Deep Learning Distribution Model Using Osmotic Computing |
|---|---|
| ID_Doc | 17860 |
| Authors | Gurgel L.; Souza A.; Cacho N.; Lopes F. |
| Year | 2023 |
| Published | Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023 |
| DOI | http://dx.doi.org/10.1109/ISC257844.2023.10293453 |
| Abstract | The utilization of edge computing has proven beneficial to Smart City applications due to its low latency processing on servers located in close proximity or even on the edge device. On the other hand, cloud computing leverages centralized computing power, resulting in significant advantages. Applications employing deep learning models commonly use both cloud computing and edge computing. While edge computing excels in providing rapid response times, devices typically possess limited computational resources. Conversely, cloud computing offers substantial computational power but suffers from longer access latencies. The challenge arises when deep learning applications must address one of these constraints, leading to poor scalability when focusing solely on edge or cloud computing or selecting a fixed distribution model. In this context, this paper aims to demonstrate the adaptive distribution of load leveraging osmotic computing. This approach directly impacts the scalability of deep learning solutions, mitigating the limitations imposed by either edge or cloud computing, improving throughput by up to 29% with low latency variation. © 2023 IEEE. |
| Author Keywords | Cloud; Distributed Deep Learning; Edge; Osmotic Computing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
33931  | 0.865 | Absardi Z.N.; Javidan R. | Iot Traffic Management Using Deep Learning Based On Osmotic Cloud To Edge Computing | Telecommunication Systems, 87, 2 (2024) |
| 1144 | 0.864 | Chaudhary N.K.; Rath A.; Babbar G.; Verma A.; Sinha S.D.; Mohapatra H. | A Critical Analysis On Edge Computing In Smart City Applications | Risk-Based Approach to Secure Cloud Migration (2025) |
| 60611 | 0.861 | Souza A.; Wen Z.; Cacho N.; Romanovsky A.; James P.; Ranjan R. | Using Osmotic Services Composition For Dynamic Load Balancing Of Smart City Applications | Proceedings - IEEE 11th International Conference on Service-Oriented Computing and Applications, SOCA 2018 (2019) |
| 5251 | 0.859 | Hoffpauir K.; Simmons J.; Schmidt N.; Pittala R.; Briggs I.; Makani S.; Jararweh Y. | A Survey On Edge Intelligence And Lightweight Machine Learning Support For Future Applications And Services | Journal of Data and Information Quality, 15, 2 (2023) |
| 5490 | 0.851 | Neha B.; Panda S.K.; Sahu P.K.; Sahoo K.S.; Gandomi A.H. | A Systematic Review On Osmotic Computing | ACM Transactions on Internet of Things, 3, 2 (2022) |
| 5117 | 0.851 | Bellavista P.; Chatzimisios P.; Foschini L.; Paradisioti M.; Scotece D. | A Support Infrastructure For Machine Learning At The Edge In Smart City Surveillance | Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019) |