Dynamic video service migration in flying edge computing networks
DOI:
https://doi.org/10.5753/jbcs.2023.2228Keywords:
MEC, FEC, QoE Support, Service Migration, UAVAbstract
Recently, Mobile Edge Computing (MEC) and service migration policies have shown promising results to improve the user experience and optimize infrastructure resources. In addition, Unmanned Aerial Vehicles (UAVs) appear as a promising solution to provide cloud service in collaboration with traditional MEC scenarios. However, in conjunction with the resources available on users' devices, contextual information has been ignored by most policies. In this article, we propose a service migration strategy based on contextual information and evaluate the influence of user mobility on migration strategies, called DVSM. Simulation results highlight the superior performance of the DVSM compared to state-of-the-art algorithms and a performance equivalent to the optimal solution when the collection and analysis of context information are carried out correctly.
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Aggarwal, S. and Kumar, N. (2019). Fog Computing for 5G-Enabled Tactile Internet: Research Issues, Challenges, and Future Research Directions. Mobile Networks and Applications, pages 1-28.
Alencar, D., Both, C., Antunes, R., Oliveira, H., Cerqueira, E., and Rosário, D. (2021). Dynamic microservice allocation for virtual reality distribution with qoe support. IEEE Transactions on Network and Service Management.
Araújo, F., Rosário, D., Cerqueira, E., and Villas, L. A. (2019). A hybrid energy-aware video bitrate adaptation algorithm for mobile networks. In 2019 15th Annual Conference on Wireless On-demand Network Systems and Services (WONS), pages 146-153. IEEE.
Araújo, F., Sobral, D., Pinheiro, S., Oliveira, H., and Rosário, D. (2021). Estratégia de migração de serviço baseada em informações contextuais em uma arquitetura mec. In Anais do XLVIII Seminário Integrado de Software e Hardware, pages 50-57. SBC.
Chen, M., Hao, Y., Hu, L., Huang, K., and Lau, V. K. (2017). Green and mobility-aware caching in 5g networks. IEEE Transactions on Wireless Communications, 16:8347-8361.
Cisco (2019). Cisco visual networking index: global mobile data traffic forecast update, 2017-2022. Update, 2017:2022.
Costa, A., Pacheco, L., Rosário, D., Villas, L., Loureiro, A. A., Sargento, S., and Cerqueira, E. (2020). Skipping-based handover algorithm for video distribution over ultra-dense vanet. Computer Networks, 176:107252.
Costanzo, F., Lorenzo, P. D., and Barbarossa, S. (2020). Dynamic resource optimization and altitude selection in uav-based multi-access edge computing. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4985-4989.
Faraci, G., Grasso, C., and Schembra, G. (2020). Fog in the clouds: Uavs to provide edge computing to iot devices. ACM Transactions on Internet Technology (TOIT), 20(3):1-26.
Gao, Y., Zhang, H., Zhu, Y., Tang, B., and Ma, H. (2017). A load-aware data migration scheme for distributed surveillance video processing with hybrid storage architecture. In 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pages 563-570.
Hao, Y., Chen, M., Cao, D., Zhao, W., Petrov, I., Antonenko, V., and Smeliansky, R. (2020). Cognitive-caching: Cognitive wireless mobile caching by learning fine-grained caching-aware indicators. IEEE Wireless Communications, 27:100-106.
Li, C., Zhu, L., Li, W., and Luo, Y. (2021). Joint edge caching and dynamic service migration in sdn based mobile edge computing. Journal of Network and Computer Applications, 177.
Li, J. et al. (2018). Modeling QoE of Virtual Reality Video Transmission over Wireless Networks. In IEEE Global Communications Conference (GLOBECOM), pages 1-7.
Montero, E., Rocha, C., Oliveira, H., Cerqueira, E., Mendes, P., Santos, A., and Rosário, D. (2021). Proactive radio-and qos-aware uav as bs deployment to improve cellular operations. Computer Networks, 200:108486.
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.
Ouyang, W., Chen, Z., Wu, J., Yu, G., and Zhang, H. (2021). Dynamic task migration combining energy efficiency and load balancing optimization in three-tier uav-enabled mobile edge computing system. Electronics (Switzerland), 10:1-30.
Pacheco, L., Oliveira, H., Rosário, D., Zhao, Z., Cerqueira, E., Braun, T., , and Mendes, P. (2021). Towards the Future of Edge Computing in the Sky:Outlook and Future Directions. In 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 220-227.
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Quer, G., Pappalardo, I., Rao, B. D., and Zorzi, M. (2018). Proactive caching strategies in heterogeneous networks with device-to-device communications. IEEE Transactions on Wireless Communications, 17:5270-5281.
Rosário, D., Schimuneck, M., Camargo, J., Nobre, J., Both, C., Rochol, J., and Gerla, M. (2018). Service migration from cloud to multi-tier fog nodes for multimedia dissemination with qoe support. Sensors, 18(2):329.
Santos, H., Alencar, D., Meneguette, R., Rosário, D., Nobre, J., Both, C., Cerqueira, E., and Braun, T. (2020). A multi-tier fog content orchestrator mechanism with quality of experience support. Computer Networks, 177:107288.
Vo, N.-S., Bui, M.-P., Truong, P. Q., Yin, C., and Masaracchia, A. (2020). Multi-tier caching and resource sharing for video streaming in 5g ultra-dense networks. IEEE Communications Letters, 7798:1-1.
Wang, S., Urgaonkar, R., Zafer, M., He, T., Chan, K., and Leung, K. K. (2019). Dynamic service migration in mobile edge computing based on markov decision process. IEEE/ACM Transactions on Networking, 27:1272-1288.
Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., and Hui, P. (2020). Edge intelligence: Architectures, challenges, and applications.
Yuan, Z. and Muntean, G.-M. (2020). Airslice: A network slicing framework for uav communications. IEEE Communications Magazine, 58(11):62-68.
Zhao, Z., Cumino, P., Esposito, C., Xiao, M., Rosário, D., Braun, T., Cerqueira, E., and Sargento, S. (2022). Smart unmanned aerial vehicles as base stations placement to improve the mobile network operations. Computer communications, 181:45-57.
Zhu, H., Cao, Y., Hu, Q., Wang, W., Jiang, T., and Zhang, Q. (2019). Multi-Bitrate Video Caching for D2D-Enabled Cellular Networks. IEEE Multimedia, 26(1):10-20.
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