[en] Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Raza, Salman
Wang, Shangguang
Ahmed, Manzoor
Anwar, Muhammad Rizwan
Mirza, Muhammad Ayzed
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Task Offloading and Resource Allocation for IoV Using 5G NR-V2X Communication
Titre traduit :
[en] Task Offloading and Resource Allocation for IoV Using 5G NR-V2X Communication
K. Jo, J. Kim, D. Kim, C. Jang, and M. Sunwoo, "Development of autonomous car-Part II: A case study on the implementation of an autonomous driving system based on distributed architecture, " IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5119-5132, Aug. 2015.
J. Feng, Z. Liu, C. Wu, and Y. Ji, "Mobile edge computing for the Internet of Vehicles: Offloading framework and job scheduling, " IEEE Veh. Technol. Mag., vol. 14, no. 1, pp. 28-36, Mar. 2019.
F. Lyu et al., "Intelligent context-aware communication paradigm design for IoVs based on data analytics, " IEEE Netw., vol. 32, no. 6, pp. 74-82, Nov./Dec. 2018.
Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: The communication perspective, " IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322-2358, 4th Quart., 2017.
P. Zhang, M. Zhou, and G. Fortino, "Security and trust issues in fog computing: A survey, " Future Gener. Comput. Syst., vol. 88, pp. 16-27, Nov. 2018.
Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, "Distributed resource allocation and computation offloading in fog and cloud networks with non-orthogonal multiple access, " IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 12137-12151, Dec. 2018.
S. Chen, Q. Li, M. Zhou, and A. Abusorrah, "Recent advances in collaborative scheduling of computing tasks in an edge computing paradigm, " Sensors, vol. 21, no. 3, p. 779, 2021.
X. Zhang, J. Zhang, Z. Liu, Q. Cui, X. Tao, and S. Wang, "Mdp-based task offloading for vehicular edge computing under certain and uncertain transition probabilities, " IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3296-3309, Mar. 2020.
K. Zhang, Y. Mao, S. Leng, Y. He, and Y. Zhang, "Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading, " IEEE Veh. Technol. Mag., vol. 12, no. 2, pp. 36-44, Jun. 2017.
L. Silva et al., "Computing paradigms in emerging vehicular environments: A review, " IEEE/CAA J. Automatica Sinica, vol. 8, no. 3, pp. 491-511, Mar. 2021.
S. Raza, S. Wang, M. Ahmed, and M. R. Anwar, "A survey on vehicular edge computing: Architecture, applications, technical issues, and future directions, " Wireless Commun. Mobile Comput., vol. 2019, Feb. 2019, Art. no. 3159762.
B. Gu and Z. Zhou, "Task offloading in vehicular mobile edge computing: A matching-theoretic framework, " IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 100-106, Sep. 2019.
C. Wu, Z. Liu, F. Liu, T. Yoshinaga, Y. Ji, and J. Li, "Collaborative learning of communication routes in edge-enabled multi-access vehicular environment, " IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 4, pp. 1155-1165, Dec. 2020.
H. Zhou, W. Xu, Y. Bi, J. Chen, Q. Yu, and X. S. Shen, "Toward 5G spectrum sharing for immersive-experience-driven vehicular communications, " IEEE Wireless Commun., vol. 24, no. 6, pp. 30-37, Dec. 2017.
Y. Dai, D. Xu, S. Maharjan, and Y. Zhang, "Joint load balancing and offloading in vehicular edge computing and networks, " IEEE Internet Things J., vol. 6, no. 3, pp. 4377-4387, Jun. 2019.
Z. Zhou, J. Feng, Z. Chang, and X. Shen, "Energy-efficient edge computing service provisioning for vehicular networks: A consensus ADMM approach, " IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 5087-5099, May 2019.
R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, "Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption, " IEEE Internet Things J., vol. 3, no. 6, pp. 1171-1181, Dec. 2016.
H. Yuan and M. Zhou, "Profit-maximized collaborative computation offloading and resource allocation in distributed cloud and edge computing systems, " IEEE Trans. Autom. Sci. Eng., vol. 18, no. 3, pp. 1277-1287, Jul. 2021.
J. Bi, H. Yuan, S. Duanmu, M. C. Zhou, and A. Abusorrah, "Energyoptimized partial computation offloading in mobile edge computing with genetic simulated-annealing-based particle swarm optimization, " IEEE Internet Things J., vol. 8, no. 5, pp. 3774-3785, Mar. 2021.
X. Chen, H. Zhang, C. Wu, S. Mao, Y. Ji, and M. Bennis, "Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning, " IEEE Internet Things J., vol. 6, no. 3, pp. 4005-4018, Jun. 2019.
J. Feng, Z. Liu, C. Wu, and Y. Ji, "AVE: Autonomous vehicular edge computing framework with aco-based scheduling, " IEEE Trans. Veh. Technol., vol. 66, no. 12, pp. 10660-10675, Dec. 2017.
T. Taleb, S. Dutta, A. Ksentini, M. Iqbal, and H. Flinck, "Mobile edge computing potential in making cities smarter, " IEEE Commun. Mag., vol. 55, no. 3, pp. 38-43, Mar. 2017.
X. Li, Y. Dang, M. Aazam, X. Peng, T. Chen, and C. Chen, "Energyefficient computation offloading in vehicular edge cloud computing, " IEEE Access, vol. 8, pp. 37632-37644, 2020.
Z. Zhou, H. Yu, C. Xu, Z. Chang, S. Mumtaz, and J. Rodriguez, "BEGIN: Big data enabled energy-efficient vehicular edge computing, " IEEE Commun. Mag., vol. 56, no. 12, pp. 82-89, Dec. 2018.
D. Ye, M. Wu, J. Kang, and R. Yu, "Optimized workload allocation in vehicular edge computing: A sequential game approach, " in Proc. Int. Conf. Commun. Netw. China, 2017, pp. 542-551.
J. Zhao, L. Wang, K.-K. Wong, M. Tao, and T. Mahmoodi, "Energy and latency control for edge computing in dense V2X networks, " 2018, arXiv:1807.02311.
[27] X. Chen, L. Jiao, W. Li, and X. Fu, "Efficient multi-user computation offloading for mobile-edge cloud computing, " IEEE/ACM Trans. Netw., vol. 24, no. 5, pp. 2795-2808, Oct. 2016.
Y. Liu, S. Wang, J. Huang, and F. Yang, "A computation offloading algorithm based on game theory for vehicular edge networks, " in Proc. IEEE Int. Conf. Commun. (ICC), 2018, pp. 1-6.
K. Zhang, Y. Mao, S. Leng, S. Maharjan, and Y. Zhang, "Optimal delay constrained offloading for vehicular edge computing networks, " in Proc. IEEE Int. Conf. Commun. (ICC), 2017, pp. 1-6.
N. Kumar, S. Zeadally, N. Chilamkurti, and A. Vinel, "Performance analysis of Bayesian coalition game-based energy-aware virtual machine migration in vehicular mobile cloud, " IEEE Netw., vol. 29, no. 2, pp. 62-69, Mar./Apr. 2015.
X. Huang, D. Ye, R. Yu, and L. Shu, "Securing parked vehicle assisted fog computing with blockchain and optimal smart contract design, " IEEE/CAA J. Automatica Sinica, vol. 7, no. 2, pp. 426-441, Mar. 2020.
W. Zhan et al., "Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing, " IEEE Internet Things J., vol. 7, no. 6, pp. 5449-5465, Jun. 2020.
J. Zhang et al., "An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing, " in Proc. IEEE 9th Int. Conf. Wireless Commun. Signal Process. (WCSP), 2017, pp. 1-6.
K. Zhang, Y. Mao, S. Leng, A. Vinel, and Y. Zhang, "Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks, " in Proc. IEEE 8th Int. Workshop Resilient Netw. Design Model. (RNDM), 2016, pp. 288-294.
R. Yu et al., "Cooperative resource management in cloud-enabled vehicular networks, " IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7938-7951, Dec. 2015.
S. Yousefi, E. Altman, R. El-Azouzi, and M. Fathy, "Analytical model for connectivity in vehicular ad hoc networks, " IEEE Trans. Veh. Technol., vol. 57, no. 6, pp. 3341-3356, Nov. 2008.
S. Durrani, X. Zhou, and A. Chandra, "Effect of vehicle mobility on connectivity of vehicular ad hoc networks, " in Proc. IEEE 72nd Veh. Technol. Conf. Fall, 2010, pp. 1-5.
I. Rasheed and F. Hu, "Intelligent super-fast vehicle-to-everything 5G communications with predictive switching between mmWave and THz links, " Veh. Commun., vol. 27, Jan. 2021, Art. no. 100303.
K. Guan et al., "On millimeter wave and THz mobile radio channel for smart rail mobility, " IEEE Trans. Veh. Technol., vol. 66, no. 7, pp. 5658-5674, Jul. 2017.
G. Naik, B. Choudhury, and J.-M. Park, "IEEE 802.11 BD & 5G NR V2X: Evolution of radio access technologies for V2X communications, " IEEE Access, vol. 7, pp. 70169-70184, 2019.
"Study on enhancement of 3GPP support for 5G V2X services (v16.2.0, release 16), " 3GPP, Sophia Antipolis, France, Rep. TR 22.886, 2019.
M. H. C. Garcia et al., "A tutorial on 5G NR V2X communications, " 2021, arXiv:2102.04538.
Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li, "Mobile-edge computing: Partial computation offloading using dynamic voltage scaling, " IEEE Trans. Commun., vol. 64, no. 10, pp. 4268-4282, Oct. 2016.
H. Wang, X. Li, H. Ji, and H. Zhang, "Federated offloading scheme to minimize latency in MEC-enabled vehicular networks, " in Proc. IEEE Globecom Workshops, 2018, pp. 1-6.
"Study on evaluation methodology of new vehicle-to-everything V2X use cases for LTE and NR (release 15), " 3GPP, Sophia Antipolis, France, Rep. TR 37.885, 2019.
S. Raza et al., "An efficient task offloading scheme in vehicular edge computing, " J. Cloud Comput., vol. 9, pp. 1-14, Jun. 2020.
J. Zhang, W. Xia, F. Yan, and L. Shen, "Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing, " IEEE Access, vol. 6, pp. 19324-19337, 2018.
H. Guo and J. Liu, "Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks, " IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4514-4526, May 2018.
Z. Li, L. Xiang, X. Ge, G. Mao, and H.-C. Chao, "Latency and reliability of mmWave multi-hop V2V communications under relay selections, " IEEE Trans. Veh. Technol., vol. 69, no. 9, pp. 9807-9821, Sep. 2020.
