Service Migration in Edge Computing Environments for Connected Autonomous Vehicles

Resumo


In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

Palavras-chave: edge computing, service migration, autonomous vehicles

Referências

Access, E. U. T. R. (2013). Study on small cell enhancements for e-utra and e-utran—higher layer aspects (release 12),”.

Aljeri, N. and Boukerche, A. (2019). Fog-enabled vehicular networks: A new challenge for mobility management. Internet Technology Letters.

Arshad, R., ElSawy, H., Sorour, S., Al-Naffouri, T. Y., and Alouini, M. (2016). Cooperative handover management in dense cellular networks. In IEEE Global Communications Conference (GLOBECOM), pages 1–6.

Bi, Y., Han, G., Lin, C., Deng, Q., Guo, L., and Li, F. (2018). Mobility support for fog computing: An sdn approach. IEEE Communications Magazine, 56(5):53–59.

Bittencourt, L. F., Lopes, M. M., Petri, I., and Rana, O. F. (2015). Towards virtual machine migration in fog computing. In 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pages 1–8. IEEE.

Chen, M., Li, W., Fortino, G., Hao, Y., Hu, L., and Humar, I. (2019). A dynamic service migration mechanism in edge cognitive computing. ACM Transactions on Internet Technology (TOIT), 19(2):30.

Chen, Y.-S. and Tsai, Y.-T. (2018). A mobility management using follow-me cloud-cloudlet in fog-computing-based rans for smart cities. Sensors, 18(2):489.

Costa, J. B., de Souza, A. M., Rosário, D., Cerqueira, E., and Villas, L. A. (2019). Efficient data dissemination protocol based on complex networks’ metrics for urban vehicular networks. Journal of Internet Services and Applications, 10(1):15.

Coutinho, R. W. and Boukerche, A. (2019). Guidelines for the Design of Vehicular Cloud Infrastructures for Connected Autonomous Vehicles. IEEE Wireless Communications, 26(4):6–11.

Filo, M., Foh, C. H., Vahid, S., and Tafazolli, R. (2020). Performance analysis of ultra-dense networks with regularly deployed base stations. IEEE Transactions on Wireless Communications.

Gao, Z., Meng, J., Wang, Q., and Yang, Y. (2018). Service migration for deadline-varying user-generated data in mobile edge-clouds. IEEE World Congress on Services, SERVICES 2018, pages 53–54.

Hwang, J., Huang, Y.-W., Vukovic, M., and Anerousis, N. (2015). Enterprise-scale cloud migration orchestrator. In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pages 1002–1007. IEEE.

Li, J., Shen, X., Chen, L., Pham Van, D., Ou, J., Wosinska, L., and Chen, J. (2019). Service Migration in Fog Computing Enabled Cellular Networks to Support Real-Time Vehicular Communications. IEEE Access, 7:13704–13714.

Li, X., Dang, Y., and Chen, T. (2018). Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power. IEEE Conference on Intelligent Transportation Systems (ITSC), 2018-Novem:3421–3426.

Liao, S., Li, J., Wu, J., Yang, W., and Guan, Z. (2019). Fog-enabled vehicle as a service for computing geographical migration in smart cities. IEEE Access, 7:8726–8736.

Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., and Shi, W. (2019). Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 107(8):1697–1716.

Lu, H., Liu, Q., Tian, D., Li, Y., Kim, H., and Serikawa, S. (2019). The Cognitive Internet of Vehicles for Autonomous Driving. IEEE Network, 33(3):65–73.

Mahmud, R., Kotagiri, R., and Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. In Internet of everything, pages 103–130. Springer.

Meneguette, R., Rodrigues, D., Costa, J., Rosario, D., and Villas, L. A. (2019). A virtual machine migration policy based on multiple attribute decision in vehicular cloud scenario. In IEEE International Conference on Communications (ICC), pages 1–6.

Miyim, A., Ismail, M., Nordin, R., and Mahardhika, G. (2013). Generic vertical handover prediction algorithm for 4g wireless networks. In IEEE International Conference on Space Science and Communication (IconSpace), pages 307–312. IEEE.

Ouyang, T., Zhou, Z., and Chen, X. (2018). Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing. IEEE Journal on Selected Areas in Communications, 36(10):2333–2345.

Rubio, L., Reig, J., and Cardona, N. (2007). Evaluation of nakagami fading behaviour based on measurements in urban scenarios. AEU-International Journal of Electronics and Communications, 61(2):135–138.

Simsek, M., Aijaz, A., Dohler, M., Sachs, J., and Fettweis, G. (2016). The 5G-Enabled Tactile Internet: Applications, requirements, and architecture. IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pages 61–66.

Sun, S., Rappaport, T. S., Rangan, S., Thomas, T. A., Ghosh, A., Kovacs, I. Z., Rodriguez, I., Koymen, O., Partyka, A., and Jarvelainen, J. (2016). Propagation path loss models for 5g urban micro-and macro-cellular scenarios. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), pages 1–6. IEEE.

Vilalta, R., Vı́a, S., Mira, F., Casellas, R., Muñoz, R., Alonso-Zarate, J., Kousaridas, A., and Dillinger, M. (2018). Control and management of a connected car using sdn/nfv, fog computing and yang data models. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pages 378–383. IEEE.

Yu, X., Guan, M., Liao, M., and Fan, X. (2019). Pre-Migration of Vehicle to Network Services Based on Priority in Mobile Edge Computing. IEEE Access, 7:3722–3730.
Publicado
07/12/2020
PACHECO, Lucas Sousa; ROSÁRIO, Denis Lima; CERQUEIRA, Eduardo Coelho; VILLAS, Leandro Aparecido. Service Migration in Edge Computing Environments for Connected Autonomous Vehicles. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 533-546. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2020.12307.