Smart City Gnosys
Smart city article details
| Title | Efficient Application Deployment In Fog-Enabled Infrastructures |
|---|---|
| ID_Doc | 22252 |
| Authors | Vijouyeh L.N.; Sabaei M.; Santos J.; Wauters T.; Volckaert B.; De Turck F. |
| Year | 2020 |
| Published | 16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020 |
| DOI | http://dx.doi.org/10.23919/CNSM50824.2020.9269052 |
| Abstract | Fog computing is a paradigm that extends cloud computing services to the edge of the network in order to support delay-sensitive Internet of Things (IoT) services. One of the most promising use-cases of fog computing is Smart City scenarios. Fog computing can substantially improve the quality of citywide services by reducing response delays. Owing to geographically distributed and resource-constrained fog nodes and a multitude of IoT devices in Smart Cities, efficient service deployment and end device traffic routing are quite challenging. Therefore, in this paper, we present an Integer Linear Programming (ILP) formulation for the Joint Application Component Placement and Traffic Routing (JAcPTR) problem in which users' delay requirements and the limited traffic processing capacity of application instances are considered. Besides, the JAcPTR enables users and infrastructure managers to easily enforce their locality and management requirements in the deployment of application instances. To cope with the considerably high execution time in large instances of the JAcPTR problem, we propose a fast polynomial-time heuristic to efficiently solve the problem. The performance of the proposed heuristic has been evaluated through extensive simulation. Results show that in large instances of the problem, while the state-of-the-art Mixed Integer Linear Programming (MILP) solver fails to obtain a solution in 50% of the simulation runs in 300 seconds, our proposed heuristic can obtain a near-optimal solution in less than one second. © 2020 IFIP. |
| Author Keywords | Application Deployment; Cloud Computing; Fog Computing; Smart City; Traffic Routing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
26780  | 0.904 | Apat H.K.; Goswami V.; Sahoo B.; Barik R.K.; Saikia M.J. | Fog Service Placement Optimization: A Survey Of State-Of-The-Art Strategies And Techniques | Computers, 14, 3 (2025) |
| 38240 | 0.894 | Aldossary M. | Multi-Layer Fog-Cloud Architecture For Optimizing The Placement Of Iot Applications In Smart Cities | Computers, Materials and Continua, 75, 1 (2023) |
| 18375 | 0.888 | Maiti P.; Apat H.K.; Kumar A.; Sahoo B.; Turuk A.K. | Deployment Of Multi-Tier Fog Computing System For Iot Services In Smart City | International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2019-December (2019) |
| 2379 | 0.887 | De Queiroz T.A.; Canali C.; Iori M.; Lancellotti R. | A Location-Allocation Model For Fog Computing Infrastructures | CLOSER 2020 - Proceedings of the 10th International Conference on Cloud Computing and Services Science (2020) |
| 1711 | 0.878 | 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) |
| 58085 | 0.876 | Nikam R.R.; Motwani D. | Towards Decentralized Fog Computing: A Comprehensive Review Of Models, Architectures, And Services | Lecture Notes in Networks and Systems, 818 (2024) |
| 20792 | 0.874 | Sharma A.; Thangaraj V. | Dmap: A Decentralized Matching Game Theory Based Optimized Internet Of Things Application Placement In Fog Computing Environment | Concurrency and Computation: Practice and Experience, 34, 23 (2022) |
| 21432 | 0.872 | Le T.V.; Le D.L.; Tran H.T. | Dynamic Traffic Optimization System: Leveraging Iot And Fog Computing For Enhanced Urban Mobility With The Rao Algorithm | Lecture Notes in Networks and Systems, 1195 LNNS (2024) |
| 38337 | 0.867 | Dalvand F.M.; Zamanifar K. | Multi-Objective Service Provisioning In Fog: A Trade-Off Between Delay And Cost Using Goal Programming | ICEE 2019 - 27th Iranian Conference on Electrical Engineering (2019) |
| 22348 | 0.866 | Santos J.; Wauters T.; Turck F.D. | Efficient Management In Fog Computing | Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023 (2023) |