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Multi-objective optimal offloading decision for multi-user structured tasks in intelligent transportation edge computing scenario

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Abstract

With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As a computing paradigm that could effectively reduce the task processing time consumption of user device and the cost of cloud server, edge computing has become an indispensable part of intelligent transportation system. However, how to reduce the load imbalance of edge computing server while ensuring that the task processing of intelligent transportation device takes less time consumption and energy consumption has become a challenge. In order to tackle this challenge, the computation offloading decision-making problem in the intelligent transportation edge computing scenario was modeled as a multi-objective optimization problem in this paper, and an adaptive multi-objective optimization algorithm (E-NSGA-III) based on NSGA-III was used to solve this problem, and comparative experiment with other methods was made. Experimental results show that compared with NSGA-II, MOEA/D and NSGA-III, proposed algorithm (E-NSGA-III) in this paper can mostly reduce time consumption by 14.28%, 18.42% and 9.82%, energy consumption by 5.59%, 6.79% and 4.83%, and load balancing variances by 21.73%, 33.46% and 18.25%.

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References

  1. Maimaris A, Papageorgiou G (2016) A review of intelligent transportation systems from a communications technology perspective. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp 54–59, https://doi.org/10.1109/ITSC.2016.7795531

  2. Ge G, Guangguang X, Tao X, Dandan L, Yunpeng W, Wei Y (2019) A survey of connected shared vehicle-road cooperative intelligent transportation systems[J]. Control Decis 34(11):2375–2389

    Google Scholar 

  3. Yang F, Wang S, Li J et al (2014) An overview of internet of vehicles[J]. China Commun 11(10):1–15

    Article  Google Scholar 

  4. Hongxing L et al (2016) Mobile edge computing: progress and challenges. In: 2016 4th IEEE International Conference on Mobile cloud Computing, Services, and Engineering (MobileCloud). IEEE

  5. Xu X, Zhang X, Liu X, et al (2020) Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles[J]. IEEE Trans Intel Transp Syst (99):1–10

  6. Jiao L, Yin H, Huang H, Guo D, Lyu Y (2018) Computation offloading for multi-user mobile edge computing. In: 2018 IEEE 20th International Conference on High Performance Computing and communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp 422–429. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00087

  7. Ning Z, Huang J, Wang X et al (2019) Mobile edge computing-enabled Internet of vehicles: toward energy-efficient scheduling[J]. IEEE Network 33(5):198–205

    Article  Google Scholar 

  8. Gao H, Huang W, Duan Y, Yang X, Zou Q (2019) Research on cost-driven services composition in an uncertain environment. J Internet Technol 20(3):755–769

    Google Scholar 

  9. Xu X, Cao H, Geng Q et al (2020) Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment[J]. Concurr Comput Practice Exp 2020:e5674

    Google Scholar 

  10. Ma H, Chen X, Zhu Z, Yu S (2020) Dynamic task offloading for moving edge computing with green energy [J]. J Comput Res Develop 57(09):1823–1838

    Google Scholar 

  11. Haibo Z, Li Hu, Shanxue C, Xiaofan He (2019) Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. J Electron Inf Technol 41(05):1194–1201

    Google Scholar 

  12. Shichao X, Zhixiu Y, Yongju X, Yun Li (2020) A distributed heterogeneous task offloading methodology for mobile edge computing [J]. J Electron Inf Technol 42(12):2891–2898

    Google Scholar 

  13. Deyi J, Like W, Chong W, Jinyang F, Yiwei R (2020) Discussion on the technology architecture and key basic support technology for intelligent mine edge-cloud collaborative computing [J]. J China Coal Soc 45(01):484–492

    Google Scholar 

  14. Chen Zhong Xu, Xiao WH, Honghao L, Xuan C (2021) Optimization strategy for offloading power tasks in residential areas based on alternate edge nodes[J]. J Zhejiang Univ Eng Sci 55(05):917–926

    Google Scholar 

  15. Zhiyong Li, Qi W, Yifan C, Guoqi X, Renfa Li (2021) A survey on task offloading research in vehicular edge computing[J]. Chin J Comput 44(05):963–982

    Google Scholar 

  16. Tian Hui Wu, Hao TY, Jianyang R, Yajuan C, Wenbao Ai, Jianhua Y (2021) Recovery mechanism of large-scale damaged edge computing network in industrial Internet of things [J]. J Commun 42(04):89–99

    Google Scholar 

  17. Lin Y, Zhang Y, Li C, Shu F (2020) Flow-of-traffic prediction program based mobile edge computing for Internet of vehicles using double auction[J]. J Commun 41(12):205–214

    Google Scholar 

  18. Li X, Wan J, Dai HN et al (2019) A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing[J]. IEEE Trans Industr Inf 15(7):4225–4234

    Article  Google Scholar 

  19. Wang K, Yu XY, Lin WL et al (2019) Computing aware scheduling in mobile edge computing system[J]. Wireless Netw 2019:1–17

    Google Scholar 

  20. Liu J, Li P, Liu J, Lai J (2019) Joint offloading and transmission power control for mobile edge computing. IEEE Access 7:81640–81651. https://doi.org/10.1109/ACCESS.2019.2921114

    Article  Google Scholar 

  21. Abbas N, Zhang Y, Taherkordi A et al (2017) Mobile edge computing: a survey[J]. IEEE Internet Things J 5(1):450–465

    Article  Google Scholar 

  22. B. Dab, N. Aitsaadi and R. Langar, "A Novel Joint Offloading and Resource Allocation Scheme for Mobile Edge Computing," 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019, pp. 1–2, doi: https://doi.org/10.1109/CCNC.2019.8651879.

  23. Dong L, Satpute MN, Shan J, Liu B, Yu Y, Yan T (2019) Computation offloading for mobile-edge computing with multi-user. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp 841–850. https://doi.org/10.1109/ICDCS.2019.00088

  24. Dai Y, Xu D, Maharjan S, Zhang Y (2018) Joint computation offloading and user association in multi-task mobile edge computing. IEEE Trans Veh Technol 67(12):12313–12325. https://doi.org/10.1109/TVT.2018.2876804

    Article  Google Scholar 

  25. Zhao J, Li Q, Gong Y et al (2019) Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks[J]. IEEE Trans Veh Technol 68(8):7944–7956

    Article  Google Scholar 

  26. Zhang K, Leng S, He Y et al (2018) Mobile edge computing and networking for green and low-latency internet of things[J]. IEEE Commun Mag 56(5):39–45

    Article  Google Scholar 

  27. Wang C, Liang C, Yu FR et al (2017) Computation offloading and resource allocation in wireless cellular networks with mobile edge computing[J]. IEEE Trans Wireless Commun 16(8):4924–4938

    Article  Google Scholar 

  28. Chen X, Jiao L, Li W et al (2015) Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Trans Networking 24(5):2795–2808

    Article  Google Scholar 

  29. He X, Ren Z, Shi C et al (2016) A novel load balancing strategy of software-defined cloud/fog networking in the internet of vehicles[J]. China Commun 13(Supplement2):140–149

    Article  Google Scholar 

  30. Zhang T, Xu Y, Loo J et al (2019) Joint computation and communication design for UAV-assisted mobile edge computing in IoT[J]. IEEE Trans Industr Inf 16(8):5505–5516

    Article  Google Scholar 

  31. Xu X, Zhang X, Gao H et al (2019) BeCome: Blockchain-enabled computation offloading for IoT in mobile edge computing[J]. IEEE Trans Industr Inf 16(6):4187–4195

    Article  Google Scholar 

  32. Xu X, Gu R, Dai F et al (2020) Multi-objective computation offloading for internet of vehicles in cloud-edge computing[J]. Wireless Netw 26(3):1611–1629

    Article  Google Scholar 

  33. Liu Q, Mo R, Xu X et al (2020) Multi-objective resource allocation in mobile edge computing using PAES for Internet of Things[J]. Wireless Netw 3:1–13

    Google Scholar 

  34. Gao H, Huang W, Yang X (2019) Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data. Intell Autom Soft Comput 25(3):547–559

    Google Scholar 

  35. Lin B, Guo W, Chen G, Xiong N, Li R (2015) Cost-driven scheduling for deadline-constrained workflow on multi-clouds. IPDPS Workshops, pp 1191–1198

  36. Deng S, Huang L, Taheri J, Zomaya AY (2015) Computation offloading for service workflow in mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(12):3317–3329

    Article  Google Scholar 

  37. Ding Y, Liu C, Zhou X, Liu Z, Tang Z (2020) A code-oriented partitioning computation offloading strategy for multiple users and multiple mobile edge computing servers. IEEE Trans Industr Inf 16(7):4800–4810. https://doi.org/10.1109/TII.2019.2951206

    Article  Google Scholar 

  38. Kuang L, Gong T, OuYang S et al (2020) Offloading decision methods for multiple users with structured tasks in edge computing for smart cities[J]. Futur Gener Comput Syst 105:717–729

    Article  Google Scholar 

  39. Cheng K, Teng Y, Sun W, Liu A, Wang X (2018) Energy-efficient joint offloading and wireless resource allocation strategy in multi-MEC server systems. IEEE Int Conf Commun (ICC) 2018:1–6. https://doi.org/10.1109/ICC.2018.8422877

    Article  Google Scholar 

  40. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601. https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  41. Zhang J, Wang S, Tang Q et al (2019) An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems[J]. Energy 172:945–957

    Article  Google Scholar 

  42. Jiao-Hong YI et al (2018) An improved NSGA-III algorithm with adaptive mutation operator for big data optimization problems. Future Gener Comput Syst 88:571–585

    Article  Google Scholar 

  43. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  44. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731. https://doi.org/10.1109/TEVC.2007.892759

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 61972666), Natural Science Foundation of Tianjin (grant number 20JCYBJC00160), Tianjin Research Innovation Project for Postgraduate Students (grant numbers 2020YJSZXS26).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin, 300384, China

    Sifeng Zhu & Mingyang Zhao

  2. Library, Tianjin Chengjian University, Tianjin, 300384, China

    Qinghua Zhang

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  1. Sifeng Zhu
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Correspondence to Sifeng Zhu.

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Zhu, S., Zhao, M. & Zhang, Q. Multi-objective optimal offloading decision for multi-user structured tasks in intelligent transportation edge computing scenario. J Supercomput 78, 17797–17825 (2022). https://doi.org/10.1007/s11227-022-04549-6

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