Computation Offloading Cost Optimization Based on Hybrid Particle Swarm Optimization Algorithm

ZHOU Tianqing; ZENG Xinliang; HU Haiqin

doi:10.11999/JEIT211390

Volume 44 Issue 9

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3065-3074

ZHOU Tianqing, ZENG Xinliang, HU Haiqin. Computation Offloading Cost Optimization Based on Hybrid Particle Swarm Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3065-3074. doi: 10.11999/JEIT211390

Citation:

ZHOU Tianqing, ZENG Xinliang, HU Haiqin. Computation Offloading Cost Optimization Based on Hybrid Particle Swarm Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3065-3074. doi: 10.11999/JEIT211390

Citation:

PDF( 2708 KB)

Computation Offloading Cost Optimization Based on Hybrid Particle Swarm Optimization Algorithm

doi: 10.11999/JEIT211390

School of Information Engineering, East China Jiaotong University, Nanchang 330013, China

Funds: The National Natural Science Foundation of China (61861017, 61861018, 61961020, 62171119), The National Key Research and Development Program of China (2020YFB1807201)

Received Date: 2021-12-01
Accepted Date: 2022-05-24
Rev Recd Date: 2022-05-08

Available Online: 2022-05-27

Publish Date: 2022-09-19

Abstract

Abstract

In order to meet the ever-increasing computation-intensive and delay-sensitive service requirements of users, as well as minimizing the processing cost of computation tasks, an optimization problem of joint task offloading, wireless resource management, and computation resource block allocation are formulated for ultra-dense heterogeneous edge computing networks under users’ delay constraints. Such a formulated problem is in a nonlinear and mixed-integer form. In order to meet the constraints and improve the convergence speed of algorithm, a Hybrid Particle Swarm Optimization (HPSO) algorithm is developed by improving Hierarchical Adaptive Search (HAS) algorithm. The simulation results show that HPSO algorithm is superior to other benchmark algorithms under users’ delay constraints, and can reduce the task processing cost effectively.
- Ultra-dense heterogeneous network,
- Edge computing,
- Resource allocation,
- Particle swarm algorithm,
- Genetic algorithm

FullText(HTML)

References(20)

References

[1]	SENG Shuming, LUO Changqing, LI Xi, et al. User matching on blockchain for computation offloading in ultra-dense wireless networks[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1167–1177. doi: 10.1109/TNSE.2020.3001081
[2]	LIN Yan, ZHANG Yijin, LI Jun, et al. Popularity-aware online task offloading for heterogeneous vehicular edge computing using contextual clustering of bandits[J]. IEEE Internet of Things Journal, 2022, 9(7): 5422–5433. doi: 10.1109/JIOT.2021.3109003
[3]	GUO Hongzhi, ZHANG Jie, LIU Jiajia, et al. Energy-aware computation offloading and transmit power allocation in ultradense IoT networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4317–4329. doi: 10.1109/JIOT.2018.2875535
[4]	GUO Fengxian, ZHANG Heli, JI Hong, et al. An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing[J]. IEEE/ACM Transactions on Networking, 2018, 26(6): 2651–2664. doi: 10.1109/TNET.2018.2873002
[5]	LIN Yan, ZHANG Rong, YANG Luxi, et al. User-centric clustering for designing ultradense networks: Architecture, objective functions, and design guidelines[J]. IEEE Vehicular Technology Magazine, 2019, 14(3): 107–114. doi: 10.1109/MVT.2019.2903741
[6]	WU Yuan, SHI Jiajun, NI Kejie, et al. Secrecy-based delay-aware computation offloading via mobile edge computing for Internet of Things[J]. IEEE Internet of Things Journal, 2019, 6(3): 4201–4213. doi: 10.1109/JIOT.2018.2875241
[7]	张海波, 李虎, 陈善学, 等. 超密集网络中基于移动边缘计算的任务卸载和资源优化[J]. 电子与信息学报, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592 ZHANG Haibo, LI Hu, CHEN Shanxue, et al. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592
[8]	PHAM Q V, LE L B, CHUNG S H, et al. Mobile edge computing with wireless backhaul: Joint task offloading and resource allocation[J]. IEEE Access, 2019, 7: 16444–16459. doi: 10.1109/ACCESS.2018.2883692
[9]	LI Huilin, XU Haitao, ZHOU Chengcheng, et al. Joint optimization strategy of computation offloading and resource allocation in multi-access edge computing environment[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 10214–10226. doi: 10.1109/TVT.2020.3003898
[10]	CHEN Min and HAO Yixue. 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. doi: 10.1109/JSAC.2018.2815360
[11]	杜剑波, 薛哪哪, 孙艳, 等. 基于NOMA的车辆边缘计算网络优化策略[J]. 物联网学报, 2021, 5(1): 19–26. doi: 10.11959/j.issn.2096-3750.2021.00207 DU Jianbo, XUE Nana, SUN Yan, et al. Optimization strategies in NOMA-based vehicle edge computing network[J]. Chinese Journal on Internet of Things, 2021, 5(1): 19–26. doi: 10.11959/j.issn.2096-3750.2021.00207
[12]	YANG Chao, LIU Yi, CHEN Xin, et al. Efficient mobility-aware task offloading for vehicular edge computing networks[J]. IEEE Access, 2019, 7: 26652–26664. doi: 10.1109/ACCESS.2019.2900530
[13]	THAI M T, LIN Y D, LAI Yuancheng, et al. Workload and capacity optimization for cloud-edge computing systems with vertical and horizontal offloading[J]. IEEE Transactions on Network and Service Management, 2020, 17(1): 227–238. doi: 10.1109/TNSM.2019.2937342
[14]	ZHOU Tianqing, QIN Dong, NIE Xuefang, et al. Energy-efficient computation offloading and resource management in ultradense heterogeneous networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(12): 13101–13114. doi: 10.1109/TVT2021.3116955
[15]	席裕庚, 柴天佑, 恽为民. 遗传算法综述[J]. 控制理论与应用, 1996, 33(6): 697–708. XI Yugeng, CHAI Tianyou, and YUN Weimin. Survey on genetic algorithm[J]. Control Theory and Applications, 1996, 33(6): 697–708.
[16]	KENNEDY J and EBERHART R. Particle swarm optimization[C]. ICNN'95-International Conference on Neural Networks, Perth, Australia, 1995: 1942–1948.
[17]	LI Meiyi, CAI Zixing, and SUN Guoyun. An adaptive genetic algorithm with diversity-guided mutation and its global convergence property[J]. Journal of Central South University of Technology, 2004, 11(3): 323–327. doi: 10.1007/s11771-004-0066-6
[18]	袁晓玲, 陈宇. 自适应权重粒子群算法在阴影光伏发电最大功率点跟踪(MPPT)中的应用[J]. 中国电力, 2013, 46(10): 85–90. doi: 10.3969/j.issn.1004-9649.2013.10.016 YUAN Xiaoling and CHEN Yu. Applications of adaptive particle swarm optimization algorithm to MPPT of shadow photovoltaic power generation[J]. Electric Power, 2013, 46(10): 85–90. doi: 10.3969/j.issn.1004-9649.2013.10.016
[19]	VAN DEN BERGH F. An analysis of particle swarm optimizers[D]. [Ph. D. dissertation], University of Pretoria, 2002.
[20]	VAN DEN BERGH F and ENGELBRECHT A P. A new locally convergent particle swarm optimiser[C]. The IEEE International Conference on Systems, Man and Cybernetics, Yasmine Hammamet, Tunisia, 2002: 96–101.