With recent advances in sensing technologies and the emerging intelligent transportation system applications, smart vehicles impose huge requirements on processing computation-intensive tasks with strict time constraints, which cannot be satisfied solely relying on local computation resources. Vehicular edge computing is an efficient paradigm for enabling low-latency and high-quality service. In this paper, we consider a multi-period task offloading scenario in vehicular edge computing environment, where tasks can be offloaded in any period during their lifetime. Then, we formulate the multi-period offloading problem (MOP) to maximize the task completion ratio, by analyzing the mobility-aware communication model, resources-aware computation model and deadline-aware award model. Further, considering the high mobility of vehicles and dynamic wireless environments, we propose a real-time distributed strategy (RtDS) to solve MOP by exploiting the collaboration among edge nodes and client vehicles. Finally, we build the simulation model based on real vehicular trajectories and give a comprehensive performance evaluation, which demonstrates the superior performance of RtDS.
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Morgan YL (2010) Notes on dsrc & wave standards suite: its architecture, design, and characteristics. IEEE Commun Surv Tutorials 12(4):504–518
Chen S, Hu J, Shi Y, Zhao L (2016) Lte-v: a td-lte-based v2x solution for future vehicular network. IEEE Intern Things J 3(6):997–1005
Chen S, Hu J, Shi Y, Zhao L, Li W (2020) A vision of c-v2x: technologies, field testing, and challenges with chinese development. IEEE Intern Things J 7(5):3872–3881
Liu K, Feng L, Dai P, Lee VC, Son SH, Cao J (2017) Coding-assisted broadcast scheduling via memetic computing in sdn-based vehicular networks. IEEE Trans Intell Transp Syst 19(8):2420–2431
Liu K, Ng JKY, Wang J, Lee VC, Wu W, Son SH (2015) Network-coding-assisted data dissemination via cooperative vehicle-to-vehicle/-infrastructure communications. IEEE Trans Intell Transp Syst 17(6):1509–1520
Liu K, Xiao K, Dai P, Lee V, Guo S, Cao J (2020) Fog computing empowered data dissemination in software defined heterogeneous vanets. IEEE Transac Mobile Comp 1. https://doi.org/10.1109/TMC.2020.2997460
Li Z, Dai Y, Chen G, Liu Y (2016) Content distribution for mobile Internet: A cloud-based approach. Springer, NewYork
Sabella D, Moustafa H, Kuure P, Kekki S, Zhou Z, Li A, Thein C, Fischer E, Vukovic I, Cardillo J et al (2017) Toward fully connected vehicles: edge computing for advanced automotive communications. 5G Automot. Assoc.(5GAA), White Paper
Liu K, Xu X, Chen M, Liu B, Wu L, Lee VC (2019) A hierarchical architecture for the future internet of vehicles. IEEE Commun Mag 57(7):41–47
Qiu H, Ahmad F, Govindan R, Gruteser M, Bai F, Kar G. (2017), Augmented vehicular reality: Enabling extended vision for future vehicles. In: Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, pp. 67–72
Xu X, Liu K, Xiao K, Feng L, Wu Z, Guo S (2020) Vehicular fog computing enabled real-time collision warning via trajectory calibration. Mob Netw Appl 25(6):2482–2494
Feng J, Liu Z, Wu C, Ji Y (2018) Mobile edge computing for the internet of vehicles: offloading framework and job scheduling. IEEE Veh Technol Mag 14(1):28–36
Wang K, Yin H, Quan W, Min G (2018) Enabling collaborative edge computing for software defined vehicular networks. IEEE Netw 32(5):112–117
Ning Z, Wang X, Huang J (2019) Mobile edge computing-enabled 5g vehicular networks: toward the integration of communication and computing. IEEE Veh Technol Mag 14(1):54–61
Zhu C, Tao J, Pastor G, Xiao Y, Ji Y, Zhou Q, Li Y, Ylä-Jääski A (2018) Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J 6(3):4150–4161
Choo S, Kim J, Pack S. (2018) , Optimal task offloading and resource allocation in software-defined vehicular edge computing. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 251–256. IEEE
Liu Y, Wang S, Zhao Q, Du S, Zhou A, Ma X, Yang F (2020) Dependency-aware task scheduling in vehicular edge computing. IEEE Internet Things J 7(6):4961–4971
Ning Z, Dong P, Wang X, Rodrigues JJPC, Xia F (2019) Deep reinforcement learning for vehicular edge computing: an intelligent offloading system. ACM Trans. Intell. Syst. Technol
Zhang K, Zhu Y, Leng S, He Y, Maharjan S, Zhang Y (2019) Deep learning empowered task offloading for mobile edge computing in urban informatics. IEEE Internet Things J 6(5):7635–7647
Liu C, Liu K, Guo S, Xie R, Lee VCS, Son SH (2020) Adaptive offloading for time-critical tasks in heterogeneous internet of vehicles. IEEE Internet Things J 7(9):7999–8011
Gu B, Zhou Z (2019) Task offloading in vehicular mobile edge computing: a matching-theoretic framework. IEEE Veh Technol Mag 14(3):100–106
Huang X, Yu R, Xie S, Zhang Y (2020) Task-container matching game for computation offloading in vehicular edge computing and networks. IEEE Transac Intel Transp Syst. https://doi.org/10.1109/TITS.2020.2990462
Wang Y, Lang P, Tian D, Zhou J, Duan X, Cao Y, Zhao D (2020) A game-based computation offloading method in vehicular multiaccess edge computing networks. IEEE Internet Things J 7(6):4987–4996
Hui Y, Su Z, Luan T, Li C, Mao G, Wu W (2020) A game theoretic scheme for collaborative vehicular task offloading in 5g hetnets. IEEE Transac Veh Technol. https://doi.org/10.1109/TVT.2020.3041587
Peng H, Shen X (2021) Multi-agent reinforcement learning based resource management in mec- and uav-assisted vehicular networks. IEEE J Sel Areas Commun 39(1):131–141
Wang J, Liu K, Li B, Liu T, Li R, Han Z (2020) Delay-sensitive multi-period computation offloading with reliability guarantees in fog networks. IEEE Trans Mob Comput 19(9):2062–2075
Wyner A (1974) Recent results in the shannon theory. IEEE Trans Inf Theory 20(1):2–10
Sadek AK, Han Z, Liu KR (2009) Distributed relay-assignment protocols for coverage expansion in cooperative wireless networks. IEEE Trans Mob Comput 9(4):505–515
Martello S, Toth P (1980) Solution of the zero-one multiple knapsack problem. Eur J Oper Res 4(4):276–283
Chekuri C, Khanna S (2005) A polynomial time approximation scheme for the multiple knapsack problem. SIAM J Comput 35(3):713–728
Liu Y, Yu H, Xie S, Zhang Y (2019) Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Trans Veh Technol 68(11):11158–11168
Wang C, Liang C, Yu FR, Chen Q, Tang L (2017) Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans Wireless Commun 16(8):4924–4938
This work was supported in part by the National Natural Science Foundation of China under Grant No.61872049, No.61802263 and No.62072064.
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Liu, C., Liu, K., Ren, H. et al. RtDS: real-time distributed strategy for multi-period task offloading in vehicular edge computing environment. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05766-5
- Vehicular edge computing
- Multi-period task offloading
- Distributed strategy