Abstract
The growing Internet of Vehicles and intelligent transportation systems pose challenges in meeting real-time application demands due to increased computation costs and problem complexity. This work embarks on the first study exploring the potential relationship between data routing and service migration in vehicular networks and aims to realize efficient joint optimization of multiple tasks. We consider a scenario where vehicles request data routing tasks and service migration tasks, which would be served via V2V/V2I communications. We propose an edge-based model that formulates the joint optimization problem for data routing and service migration. This model considers the heterogeneous transmission and computation resources of edge nodes and vehicles, the mobility of vehicles, aiming at maximizing both the completion rate of data routing tasks and service migration tasks. Furthermore, we propose a novel Location Mapping based Evolutionary Multitasking (LM-EMT) algorithm. This algorithm uses different integer-based coding schemes for each of the two problems, and utilizes an explicit knowledge transfer strategy to exploit problem dependence for accelerated solving. We design a two-stage transferring strategy to mitigate the negative effects between solutions. Finally, we build a simulation model and conduct a comprehensive performance evaluation to verify the superiority of the proposed algorithm.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172064, the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202200503), and the Chongqing Young-Talent Program (Project No. cstc2022ycjh-bgzxm0039).
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Zhou, Y., Ren, H., Xiao, K., Liu, K. (2023). Joint Data Routing and Service Migration via Evolutionary Multitasking Optimization in Vehicular Networks. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_31
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DOI: https://doi.org/10.1007/978-981-99-5847-4_31
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