Skip to main content

Advertisement

SpringerLink
Log in

An approach of multi-objective computing task offloading scheduling based NSGS for IOV in 5G

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

As a new technology, Internet of Vehicles (IoV) needs high bandwidth and low delay. However, the current on-board mobile terminal equipment cannot meet the needs of the IoV. Therefore, using mobile edge computing (MEC) can solve the problems of energy consumption and time delay in the IoV. In the MEC, task offloading can solve the problem of resource constraint on mobile devices effectively, but it is not optimal to offload all tasks to edge servers. In this paper, the vehicle computation task is regarded as a directed acyclic graph (DAG), and task nodes’ execution location and scheduling order are optimized. Considering the energy consumption and delay of the system, the vehicle computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then a Non-dominated Sorting Genetic Strategy(NSGS) is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay and energy consumption. Finally, a large number of experiments are carried to prove the performance of the algorithm. The experimental results show that the algorithm can make the optimal decision in practical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability

Enquiries about data availability should be directed to the authors.

References

  1. Atat, R., Liu, L., Chen, H., Wu, J., Li, H., Yi, Y.: Enabling cyber-physical communication in 5g cellular networks: challenges, spatial spectrum sensing, and cyber-security. IET Cyber-Phys. Syst.: Theory Appl. 2(1), 49–54 (2017)

    Article  Google Scholar 

  2. Peng, X., Ren, J., She, L., Zhang, D., Li, J., Zhang, Y.: Boat: a block-streaming app execution scheme for lightweight iot devices. IEEE Internet of Things J. 1 (2018)

  3. Pan, J., Mcelhannon, J.: Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things J. 1 (2017)

  4. Hai, L.A., Sz, B., Zc, A., Hl, C., Lw, D.: A survey on computation offloading modeling for edge computing - sciencedirect. J. Netw. Comput. Appl. 169, (2020)

  5. Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. PP(3), 1 (2017)

    Google Scholar 

  6. Beck, M.T., Maier, M.: Mobile edge computing: challenges for future virtual network embedding algorithms. (2014)

  7. Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., Bennis, M.: Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet of Things J. 1 (2018)

  8. Shi, W., Jie, C., Quan, Z., Li, Y., Xu, L.: Edge computing: vision and challenges. Internet Things J. IEEE 3(5), 637–646 (2016)

    Article  Google Scholar 

  9. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K..B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. PP(99), 1 (2017)

    Google Scholar 

  10. Dinh, T.Q., La, Q.D., Quek, T., Shin, H.: Learning for computation offloading in mobile edge computing. IEEE Trans. Commun. 66(12), 6353–6367 (2018)

    Article  Google Scholar 

  11. Zhang, D., Piao, M., Zhang, T., Chen, C., Zhu, H.: New algorithm of multi-strategy channel allocation for edge computing. AEU-Int. J. Electron. C. 126, 153372 (2020)

    Article  Google Scholar 

  12. Cui, Y., Zhang, D., Zhang, T., Chen, L., Zhu, H.: Novel method of mobile edge computation offloading based on evolutionary game strategy for iot devices. AEU - Int. J. Electron. Commun. 118 (2020)

  13. Liu, S., Zhang, D., Liu, X., Zhang, T., Wu, H.: Adaptive repair algorithm for tora routing protocol based on flood control strategy. Comput. Commun. 151, 437–448 (2020)

    Article  Google Scholar 

  14. You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading (extended version). (2016)

  15. Liu, Y., Peng, M., Shou, G., Chen, Y., Chen, S.: Towards edge intelligence: multi-access edge computing for 5g and internet of things. IEEE Internet Things J. 7(8), 6722–6747 (2020)

    Article  Google Scholar 

  16. Wang, F., Xu, J., Wang, X., Cui, S.: Joint offloading and computing optimization in wireless powered mobile-edge computing systems. In: 2017 IEEE International Conference on Communications (ICC), (2017)

  17. Shuai, Y., Xin, W., Langar, R.: Computation offloading for mobile edge computing: a deep learning approach. In: IEEE International Symposium on Personal, (2017)

  18. Zhang, Y., Niyato, D., Wang, P.: Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Trans. Mob. Comput. Preprints(12), 2516–2529 (2015)

    Article  Google Scholar 

  19. Zheng, J., Cai, Y., Wu, Y., Shen, X.: Dynamic computation offloading for mobile cloud computing: a stochastic game-theoretic approach. IEEE Trans. Mob. Comput. PP(4), 1 (2019)

    Google Scholar 

  20. Cheng, J., Mi, H., Huang, Z., Gao, S., Liu, C.: Connectivity modeling and analysis for internet of vehicles in urban road scene. IEEE Access PP(99), 1 (2017)

    Google Scholar 

  21. Xiao, M., Lin, C., Xiang, X., Chen, C.: Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing. In: Acm International Conference, (2015)

  22. Wang, Y., Min, S., Wang, X., Liang, W., Li, J.: Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans. Commun. 64(10), 4268–4282 (2016)

    Google Scholar 

  23. Mahmoodi, S.E., Subbalakshmi, K.P., Sagar, V.: Cloud offloading for multi-radio enabled mobile devices. In: IEEE International Conference on Communications, (2015)

  24. Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot-edge-cloud computing environments. IEEE Trans. Parallel Distrib. Syst. PP(99), 1 (2019)

    Google Scholar 

  25. Kwak, J., Kim, Y., Lee, J., Chong, S.: Dream: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015)

    Article  Google Scholar 

  26. Wang, J., Jie, P., Wei, Y., Liu, D., Fu, J.: Adaptive application offloading decision and transmission scheduling for mobile cloud computing. IEEE International Conference on Communications, (2016)

  27. Dinh, T.Q., Tang, J., La, Q.D., Quek, T.: Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications, (2017)

  28. Cui, L., Xu, C., Yang, S., Huang, J.Z., Lu, N.: Joint optimization of energy consumption and latency in mobile edge computing for internet of things. IEEE Internet of Things J. 99, (2018)

  29. Wu, H., Knottenbelt, W., Wolter, K.: “An efficient application partitioning algorithm in mobile environments,” IEEE Transactions on Parallel and Distributed Systems, 1464–1480 (2019)

  30. Liu, L..Y., Tan, H..S.: Dependent task placement and scheduling with function configuration in edge computing. In: 2019 IEEE/ACM 27th International Symposium on Quality of Service(IWQoS) 1(1), 1–10 (2019)

  31. Liu, J..H., Zhang, Q.: Reliability and latency aware code-partitioning offloading in mobile edge computing. In: 2019 IEEE Wireless Communication and Networking Conference(WCNC) 1(1), 1–7 (2019)

  32. Rui, L.L.: Computation offloading in a mobile edge communication network: A joint transmission delay and energy consumption dynamic awareness mechanism. IEEE Internet Things J. 1(1), 99 (2019)

    Google Scholar 

  33. Vachhani, V.L., Dabhi, V.K., Prajapati, H.B.: Improving nsga-ii for solving multi objective function optimization problems. In: 2016 International Conference on Computer Communication and Informatics, (2016)

  34. Gao, J.X.: Novel approach of distributed adaptive trust metrics for manet. Wireless Netw. 25(6), 3587–3603 (2019)

    Article  Google Scholar 

  35. Zhang, D.G., Li, G., Zheng, K.: An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans. Industr. Inf. 10(1), 766–773 (2014)

    Article  Google Scholar 

  36. Liu, S.: Novel unequal clustering routing protocol considering energy balancing based on network partition distance for mobile education. J. Netw. Comput. Appl. 88(15), 1–9 (2017)

    Google Scholar 

  37. Zhang, T.: Novel self-adaptive routing service algorithm for application of vanet. Appl. Intell. 49(5), 1866–1879 (2019)

    Article  Google Scholar 

  38. Wang, X., Song, X.D.: A novel approach to mapped correlation of id for rfid anti-collision. IEEE Trans. Serv. Comput. 7(4), 741–748 (2014)

    Article  MathSciNet  Google Scholar 

  39. Yang, J.N., Mao, G.Q.: Optimal base station antenna downtilt in downlink cellular networks. IEEE Trans. Wireless Commun. 18(3), 1779–1791 (2019)

    Article  Google Scholar 

  40. Cui, Y.Y., Zhang, T.: New quantum-genetic based olsr protocol (qg-olsr) for mobile ad hoc network. Appl. Soft Comput. 80(7), 285–296 (2019)

    Google Scholar 

  41. Ge, H.: New multi-hop clustering algorithm for vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 20(4), 1517–1530 (2019)

    Article  Google Scholar 

  42. Chen, J.Q., Mao, G.Q.: Capacity of cooperative vehicular networks with infrastructure support:multi-user case. IEEE Trans. Veh. Technol. 67(2), 1546–1560 (2018)

    Article  Google Scholar 

  43. Zhang, T., Zhang, J.: A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J. Wirel. Commun. Netw. 2018(159), 1–15 (2018)

    Google Scholar 

  44. Zhang, T.: A new method of data missing estimation with fnn-based tensor heterogeneous ensemble learning for internet of vehicle. Neurocomputing 420(1), 98–110 (2021)

    Article  Google Scholar 

  45. Chen, L., Zhang, J.: A multi-path routing protocol based on link lifetime and energy consumption prediction for mobile edge computing. IEEE Access 8(1), 69058–69071 (2020)

    MathSciNet  Google Scholar 

  46. Chen, C., Cui, Y.Y.: New method of energy efficient subcarrier allocation based on evolutionary game theory. Mobile Netw. Appl. 26(2), 523–536 (2021)

    Article  Google Scholar 

  47. Zhu, Y.N.: A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (iot). Comput. Mathem. Appl. 64(5), 1044–1055 (2012)

    MATH  Google Scholar 

  48. Zhou, S.: A low duty cycle efficient mac protocol based on self-adaption and predictive strategy. Mobile Netw. Appl. 23(4), 828–839 (2018)

    Article  Google Scholar 

  49. Niu, H.L.: Novel peecr-based clustering routing approach. Soft. Comput. 21(24), 7313–7323 (2017)

    Article  Google Scholar 

  50. Zheng, K., Zhang, T.: A novel multicast routing method with minimum transmission for wsn of cloud computing service. Soft. Comput. 19(7), 1817–1827 (2015)

    Article  Google Scholar 

  51. Gong, C. L.: A new algorithm of clustering aodv based on edge computing strategy in iov. Wireless Netw. 27(4), 2891–2908 (2021)

    Article  Google Scholar 

  52. Liu, X.: Novel best path selection approach based on hybrid improved a* algorithm and reinforcement learning. Appl. Intell. 51(9), 1–15 (2021)

    Google Scholar 

  53. Ni, C. H., Zhang, J.: A kind of novel edge computing architecture based on adaptive stratified sampling. Comput. Commun. 183(2022), 121–135 (2022)

    Google Scholar 

  54. Zheng, K., Zheng, T.: A novel multicast routing method with minimum transmission for wsn of cloud computing service. Soft. Comput. 19(7), 1817–1827 (2015)

    Article  Google Scholar 

  55. Zhang, X.D.: Design and implementation of embedded un-interruptible power supply system (eupss) for web-based mobile application. Enterprise Inform. Syst. 6(4), 473–489 (2012)

    Article  Google Scholar 

  56. Zhang, X.D.: Novel dynamic source routing protocol (dsr) based on genetic algorithm-bacterial foraging optimization (ga-bfo). Int. J. Commun. Syst. 31(18), 1–20 (2018)

    Article  Google Scholar 

Download references

Funding

This research work is supported by National Natural Science Foundation of China (Grant No. 61571328), Tianjin Key Natural Science Foundation (No.13JCZDJC34600, 18JCZDJC96800, Major projects of science and technology in Tianjin (No.15ZXDSGX 00050), Major projects of science and technology for their services in Tianjin (No.16ZXFWGX00010, No.17YFZCGX00360), the Key Subject Foundation of Tianjin (15JCYB JC46500).

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Jie Zhang

  2. Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Ming-jie Piao, De-gan Zhang & Wen-miao Dong

  3. School of Sports Economics and Management, Tianjin University of Sport, Tianjin, 301617, China

    Ting Zhang

Authors
  1. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-jie Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. De-gan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wen-miao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JZ: conceptualization; MJP: data curation, methodology, writing—original draft preparation; DGZ: writing—review and editing; TZ and WMD: writing—reviewing and editing. All data included in this study are available upon request by contact with the corresponding author.

Corresponding author

Correspondence to Ming-jie Piao.

Ethics declarations

Conflict of interest

The authors have not disclosed any competing interests.

Reasearch involved in human participants or animals participants

This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Piao, Mj., Zhang, Dg. et al. An approach of multi-objective computing task offloading scheduling based NSGS for IOV in 5G. Cluster Comput 25, 4203–4219 (2022). https://doi.org/10.1007/s10586-022-03635-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-022-03635-x

Keywords

Search

Navigation