The Fog Node Location Problem
ResumoThis paper summarizes the thesis “The Fog Node Location Problem”, which attempted to answer the question of how fog nodes should be located to process end-user demands that are variable in time and space. The problem was studied from different perspectives, optimizing the number of served users, deployment costs, energy consumption, and latency. This research considered both fixed servers and mobile fog nodes mounted on unmanned aerial vehicles (UAVs). The thesis has introduced several contributions, including linear programming models and novel algorithms. Results showed that the proposed solutions were quite efficient to design a fog computing infrastructure and that UAVs are suitable to be used as fog nodes.
da Silva, R. A. C. and da Fonseca, N. L. S. (2019). On the location of fog nodes in fog-cloud infrastructures. Sensors, 19(11).
da Silva, R. A. C. and da Fonseca, N. L. S. (2020). Location of fog nodes for reduction of energy consumption of end-user devices. IEEE Transactions on Green Communications and Networking, 4(2):593–605.
da Silva, R. A. C. and da Fonseca, N. L. S. (2022a). Design of fog computing infrastructures with rotary-wing uavs. In GLOBECOM 2022 2022 IEEE Global Communications Conference, pages 5789–5794.
da Silva, R. A. C. and da Fonseca, N. L. S. (2022b). The Fog Node Location Problem. PhD thesis, Universidade Estadual de Campinas.
da Silva, R. A. C. and da Fonseca, N. L. S. (2023). Location of fog nodes mounted on fixed-wing UAVs. Vehicular Communications, 41:100600.
da Silva, R. A. C., da Fonseca, N. L. S., and Boutaba, R. (2021). Evaluation of the employment of uavs as fog nodes. IEEE Wireless Communications, 28(5):20–27.
da Silva, R. A. C. and Fonseca, N. L. S. d. (2018). Resource allocation mechanism for a fog-cloud infrastructure. In 2018 IEEE International Conference on Communications (ICC), pages 1–6.
Kim, W. and Chung, S. (2018). User-participatory fog computing architecture and its management schemes for improving feasibility. IEEE Access, 6:20262–20278.
Lago, D. G., da Silva, R. A., Madeira, E. R., da Fonseca, N. L., and Medhi, D. (2021). Sinergycloud: A simulator for evaluation of energy consumption in data centers and hybrid clouds. Simulation Modelling Practice and Theory, 110:102329.
Larumbe, F. and Sansò, B. (2012). Cloptimus: A multi-objective cloud data center and software component location framework. In 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), pages 23–28.
Larumbe, F. and Sansò, B. (2013). A tabu search algorithm for the location of data centers and software components in green cloud computing networks. IEEE Transactions on Cloud Computing, 1(1):22–35.
Lähderanta, T., Leppänen, T., Ruha, L., Lovén, L., Harjula, E., Ylianttila, M., Riekki, J., and Sillanpää, M. J. (2021). Edge computing server placement with capacitated location allocation. Journal of Parallel and Distributed Computing, 153:130–149.
Montoya-Munoz, A. I., da Silva, R. A., Rendon, O. M. C., and da Fonseca, N. L. (2022). Reliability provisioning for fog nodes in smart farming iot-fog-cloud continuum. Computers and Electronics in Agriculture, 200:107252.
Wang, J., Liu, K., and Pan, J. (2020). Online UAV-mounted edge server dispatching for mobile-to-mobile edge computing. IEEE Internet Things J., 7(2):1375–1386.
Zhao, Z., Min, G., Gao, W., Wu, Y., Duan, H., and Ni, Q. (2018). Deploying edge computing nodes for large-scale IoT: A diversity aware approach. IEEE Internet of Things Journal, 5(5):3606–3614.
Zhou, Y., Pan, C., Yeoh, P. L., Wang, K., Elkashlan, M., Vucetic, B., and Li, Y. (2021). Communication-and-computing latency minimization for UAV-enabled virtual reality delivery systems. IEEE Transactions on Communications, 69(3):1723–1735.