Fatiamento Dinâmico de Redes em Computação em Névoa para Usuários Móveis

• Diogo M. Gonçalves Unicamp
• Luiz F. Bittencourt Unicamp
• Edmundo R. M. Madeira Unicamp

DOI: https://doi.org/10.5753/sbrc.2021.16711

Abstract

Fog Computing environments provide computing resources at the edge of the network. Based on the resource virtualization, Virtual Networks can be created on-demand over one shared physical infrastructure to serve the users. In such a context, a performance analysis of these networks on different scenarios is required to identify the strengths and weaknesses of that technology. This work fills that gap by analyzing different resource allocation approaches for network slicing, considering user' mobility support. Simulations made on MobFogSim show that static resource allocation may present performance issues due to variability of resource demand over time. Dynamic resource allocation is shown as a possible solution to that scenario. However, that approach is sensitive to a computing overhead.

References

Addad, R. A., Taleb, T., Flinck, H., Bagaa, M., and Dutra, D. (2020). Network Slice Mobility in Next Generation Mobile Systems: Challenges and Potential Solutions. IEEE Network, 34(1):84–93.

Behrisch, M., Bieker, L., Erdmann, J., and Krajzewicz, D. (2011). SUMO Simulation In 3rd International Conference on Advances in of Urban Mobility: An Overview. System Simulation (SIMUL).

Codeca, L., Frank, R., and Engel, T. (2015). Luxembourg SUMO Trafc (LuST) Scenario: 24 Hours of Mobility for Vehicular Networking Research. In IEEE Conference on Vehicular Networking (VNC), pages 1–8.

Gonçalves, D., Puliato, C., Mingozzi, E., Rana, O., Bittencourt, L., and Madeira, E. (2020). Dynamic network slicing in fog computing for mobile users in mobfogsim. In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), pages 237–246. IEEE.

Gonçalves, D. M., Bittencourt, L. F., and Madeira, E. R. M. (2018). Migração proativa de máquinas virtuais para aplicações móveis na computação em névoa. In Simpósio Brasileiro de Redes de Computadores (SBRC), volume 36.

Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in the Internet of Things, Edge and Fog Computing Environments. Software: Practice and Experience, 47(9):1275–1296.

Lopes, M. M., Higashino, W. A., Capretz, M. A., and Bittencourt, L. F. (2017). MyiFogSim: A Simulator for Virtual Machine Migration in Fog Computing. In ACM 6th International Workshop on Clouds and (eScience) Applications Management (CloudAM). Companion Proceedings of the 10th International Conference on Utility and Cloud Computing, pages 47–52.

Puliato, C., Goncalves, D. M., Lopes, M. M., Martins, L. L., Madeira, E., Mingozzi, E., Rana, O., and Bittencourt, L. F. (2020). Mobfogsim: Simulation of mobility and migration for fog computing. Simulation Modelling Practice and Theory, 101:102062.

Raza, M. R., Fiorani, M., Rostami, A., Öhlen, P., Wosinska, L., and Monti, P. (2018). Dynamic Slicing Approach for Multi-tenant 5G Transport Networks. IEEE/OSA Journal of Optical Communications and Networking, 10(1):A77–A90.

Xiong, K., Leng, S., Hu, J., Chen, X., and Yang, K. (2019). Smart Network Slicing for Vehicular Fog-RANs. IEEE Transactions on Vehicular Technology, 68(4):3075–3085.

Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A., and Leung, V. C. (2017). Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges. IEEE Communications Magazine, 55(8):138–145.