To meet the requirements of offloading time optimization for computing tasks and load balance optimization for edge devices, an intelligent computing offloading method (ICOM) is proposed in this paper. Initially, a computing offloading model based on the real-world scenario is erected. Besides, the time model of task execution and the load balance model of edge devices are also established. Then, the non-dominant sorting genetic algorithm (NSGA-II) is used to realize the joint optimization of the offloading delay of computing tasks and the load balance of edge devices, so as to find effective computing offloading strategies for computing tasks. Finally, the multi-criteria decision making (MCDM) and the technique for order preference by similarity to an ideal solution (TOPSIS) are utilized to select the optimal computing offloading strategy. Experimental results show that ICOM enables computing tasks to be completed within the expected time, while also ensuring load balance of edge devices.
[1] 张文丽, 郭兵, 沈艳, 等. 智能移动终端计算迁移研究[J]. 计算机学报, 2016, 39(5):1021-1038. Zhang W L, Guo B, Shen Y, et al. Computation offloading on intelligent mobile terminal[J]. Chinese Journal of Computers, 2016, 39(5):1021-1038. (in Chinese)
[2] Xu X L, Xue Y, Qi L Y, et al. An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles[J]. Future Generation Computer Systems, 2019, 96:89-100.
[3] 胡海洋, 刘润华, 胡华. 移动云计算环境下任务调度的多目标优化方法[J]. 计算机研究与发展, 2017, 54(9):1909-1919. Hu H Y, Liu R H, Hu H. Multi-object optimization for task scheduling in mobile cloud computing[J]. Journal of Computer Research and Development, 2017, 54(9):1909-1919. (in Chinese)
[4] Wang K, Yin H, Quan W, et al. Enabling collaborative edge computing for software defined vehicular networks[J]. IEEE Network, 2018, 32(5):112-117.
[5] Hou X W, Ren Z Y, Wang J J, et al. Reliable computation offloading for edge computingenabled software-defined IoV[J]. IEEE Internet of Things Journal, 2020, 7(8):7097-7111.
[6] Hu S H, Li G H. Dynamic request scheduling optimization in mobile edge computing for IoT applications[J]. IEEE Internet of Things Journal, 2019, 7(2):1426-1437.
[7] Zhang W Y, Zhang Z J, Chao H C. Cooperative fog computing for dealing with big data in the internet of vehicles:architecture and hierarchical resource management[J]. IEEE Communications Magazine, 2017, 55(12):60-67.
[8] Qiao G H, Leng S P, Zhang K, et al. Collaborative task offloading in vehicular edge multiaccess networks[J]. IEEE Communications Magazine, 2018, 56(8):48-54.
[9] 董莉, 宋晓勤, 韩杰. 基于遗传粒子群优化的认知OFDM网络资源分配算法[J]. 应用科学学报, 2017, 35(3):288-298. Dong L, Song X Q, Han J. Resource allocation based on genetic algorithm and particle swarm optimization for cognitive OFDM network[J]. Journal of Applied Sciences, 2017, 35(3):288-298. (in Chinese)
[10] Zhu K L, Chen Z C, Peng Y Y, et al. Mobile edge assisted literal multi-dimensional anomaly detection of in-vehicle network using LSTM[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5):4275-4284.
[11] Chen L X, Zhou S, Xu J. Computation peer offloading for energy-constrained mobile edge computing in small-cell networks[J]. IEEE/ACM Transactions on Networking, 2018, 26(4):1619-1632.
[12] Taleb T, Samdanis K, Mada B, et al. On multi-access edge computing:a survey of the emerging 5G network edge cloud architecture and orchestration[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3):1657-1681.
[13] Pu L J, Chen X, Mao G Q, et al. Chimera:an energy-efficient and deadline-aware hybrid edge computing framework for vehicular crowdsensing applications[J]. IEEE Internet of Things Journal, 2018, 6(1):84-99.
[14] Guo H, Liu J J. Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5):4514-4526.
[15] Liu Y, Yu H M, Xie S L, et al. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(11):11158-11168.
[16] Ning Z L, Huang J, Wang X J, et al. Mobile edge computing-enabled internet of vehicles:toward energy-efficient scheduling[J]. IEEE Network, 2019, 33(5):198-205.
[17] Yu C Q, Lin B, Guo P, et al. Deployment and dimensioning of fog computing-based internet of vehicle infrastructure for autonomous driving[J]. IEEE Internet of Things Journal, 2018, 6(1):149-160.
[18] Chang Z, Zhou Z Y, Ristaniemi T, et al. Energy efficient optimization for computation offloading in fog computing system[C]//2017 IEEE Global Communications Conference (GLOBECOM). IEEE, 2017:1-6.
[19] Mao Y Y, Zhang J, Song S H, et al. Power-delay tradeoff in multi-user mobile-edge computing systems[C]//2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 2016:1-6.
[20] Le H Q, Hessein A, Klein A. Efficient resource allocation in mobile-edge computation offloading:Completion time minimization[C]//2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017:2513-2517.
[21] Wang X, Ning Z, Wang L. Offloading in Internet of vehicles:a fog-enabled real-time traffic management system[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10):4568-4578.
[22] Dinh T Q, Tang J H, La Q D, et al. Offloading in mobile edge computing:task allocation and computational frequency scaling[J]. IEEE Transactions on Communications, 2017, 65(8):3571-3584.
[23] Chen X, Jiao L, Li W, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE ACM Transactions on Networking, 2016, 24(5):2795-2808.
[24] Chen M, Hao Y. Task offloading for mobile edge computing in software defined ultra-dense network[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(3):587-597.
