物联网边缘计算场景下基于优先级任务的卸载决策优化

doi:10.13229/j.cnki.jdxbgxb.20230047

摘要/Abstract

摘要：

针对物联网应用场景下，依照最大容忍时延等标量信息划分任务卸载优先级的方式难以满足紧急任务处理需求的问题，为确保关键程度最高的紧急任务优先处理，本文提出基于任务关键程度划分优先级的方法。针对优先级任务卸载决策问题展开了研究，考虑了边缘服务器任务处理程序缓存，以最小化综合时延、社会损失率、负载失衡度为优化目标，建立了多目标优化任务卸载决策问题模型，提出了一种改进的多目标灰狼优化算法求解问题。该算法引入了灰狼个体尽力而为进化策略、基于改进差分进化算子的外部存档生成策略、加权最值法最优解保存策略以提升算法性能。仿真实验表明：本文提出的方法能有效降低综合时延和社会损失率，优化边缘服务器间负载均衡，确保紧急任务优先处理，且其性能均较其他方法表现优异。

关键词: 物联网, 边缘计算, 任务卸载决策, 优先级任务, 多目标灰狼优化算法

Abstract:

In the application scenario of the Internet of Things， it is difficult to meet the processing needs of emergency tasks by prioritizing task offloading based on scalar information such as maximum tolerance delay. The most critical task is called an emergency task. To ensure that emergency tasks are prioritized， this paper proposes a method of prioritizing tasks based on their criticality， and conducts research on the decision-making problem of priority task offloading， taking into account the caching of edge server task handlers， with the optimization objectives of minimizing comprehensive delays， social loss rate， and load imbalance degree. A multi-objective optimization task offloading decision problem model was established， and an improved multi-objective grey wolf optimizer was proposed to solve the problem. This algorithm introduces the best effort evolution strategy of grey wolf individuals， an external archive generation strategy based on improved differential evolution operator， and a weighted maximum method optimal solution preservation strategy to improve algorithm performance. Simulation experiments show that the algorithm proposed in this paper can effectively reduce the comprehensive delay and social loss rate， optimize load balancing between edge servers， ensure priority processing of emergency tasks， and its algorithm performance is superior to other algorithm schemes.

Key words: Internet of things, edge computing, task offloading decision, priority task, multi-objective grey wolf optimizer

中图分类号:

TP393.1

朱思峰,胡家铭,杨诚瑞,蔡江昊. 物联网边缘计算场景下基于优先级任务的卸载决策优化[J]. 吉林大学学报(工学版), 2024, 54(11): 3338-3350.

Si-feng ZHU,Jia-ming HU,Cheng-rui YANG,Jiang-hao CAI. Optimization of offloading decision based on priority task in edge computing scenes of internet of things[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3338-3350.

图/表 13

图1

图2

图3

表1

参数设置"

参数	描述	取值
$f i u$	$u i$ 的计算能力	Rand（0.3，0.31）GHz
$d i u p$	$s i$ 数据部分的上传数据量	Rand（10，30）MB
$D P i$	$s i$ 的处理程序数据量	Rand（200，400）MB
$d i d o w n$	$s i$ 的回传数据量	Rand（1，20）MB
$t i e n d u r e$	$s i$ 的最大容忍时延	Rand（4，6）s
$p r i$	$s i$ 的优先级	｛1，2，3｝
$B E 1$	$P R 1$ 任务的社会收益	Rand（18，20）
$B E 2$	$P R 2$ 任务的社会收益	Rand（7，9）
$B E 3$	$P R 3$ 任务的社会收益	Rand（1，3）
$f j e$	$e j$ 的计算能力	Rand（10，20）GHz
$T 0 p r o$	$e j$ 上的剩余任务处理时延	Rand（0，0.2）s

表1

表2

不同任务数量下4种算法所得HV平均值"

方案	MPSO/D	MOGWO/D	MO-NSGA	IMOGWO
N	Mean	Mean	Mean	Mean
50	6.641 9e-1-	5.964 1e-1-	6.625 1e-1-	6.860 7e-1
100	5.870 4e-1-	5.019 3e-1-	5.913 2e-1-	6.247 8e-1
200	4.194 7e-1-	3.726 0e-1-	4.207 3e-1-	4.794 6e-1
400	3.081 3e-1-	2.645 5e-1-	3.197 5e-1-	3.646 0e-1
600	2.567 0e-1-	2.121 9e-1-	2.688 0e-1-	3.288 6e-1
+/-/ $≈$	0/5/0	0/5/0	0/5/0

表2

表3

不同边缘服务器数量下4种算法所得HV平均值"

方案	MPSO/D	MOGWO/D	MO-NSGA	IMOGWO
M	Mean	Mean	Mean	Mean
2	2.630 8e-1-	1.651 9e-1-	2.501 9e-1-	3.195 6e-1
4	3.057 2e-1-	2.591 9e-1-	3.222 0e-1-	3.670 4e-1
6	3.364 1e-1-	3.162 2e-1-	3.486 6e-1-	3.897 2e-1
8	3.081 3e-1-	2.645 5e-1-	3.197 5e-1-	3.646 0e-1
10	3.564 3e-1-	3.119 0e-1-	3.379 9e-1-	4.120 7e-1
+/-/方正汇总行 $≈$	0/5/0	0/5/0	0/5/0

表3

图4

图5

图6

图7

图8

图9

图10

参考文献 25

1	Liu P, Zhang Y F, Fu T T, et al. Intelligent mobile edge caching for popular contents in vehicular cloud toward 6G[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 5265-5274.
2	Sabella D, Vaillant A, Kuure P, et al. Mobile-edge computing architecture: the role of MEC in the internet of things[J]. IEEE Consumer Electronics Magazine, 2016, 5(4): 84-91.
3	Khan L U, Yaqoob I, Tran N H, et al. Edge-computing-enabled smart cities: a comprehensive survey[J]. IEEE Internet of Things Journal, 2020, 7(10): 10200-10232.
4	Li M, Xiong N X, Zhang Y, et al. Priority-MECE: a mobile edge cloud ecosystem based on priority tasks offloading[J]. Mobile Networks and Applications, 2022, 27(3): 1768-1777.
5	Xu X L, Gu R H, Dai F, et al. Multi-objective computation offloading for internet of vehicles in cloud-edge computing[J]. Wireless Networks, 2020, 26: 1611-1629.
6	朱思峰, 赵明阳, 柴争义. 边缘计算场景中基于粒子群优化算法的计算卸载[J]. 吉林大学学报: 工学版, 2022, 52(11): 2698-2705.
	Zhu Si-feng, Zhao Ming-yang, Chai Zheng-yi. Computing offloading scheme based on particle swarm optimization algorithm in edge computing scene[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(11): 2698-2705.
7	Liu Q, Mo R C, Xu X L, et al. Multi-objective resource allocation in mobile edge computing using PAES for internet of things[J]. Wireless Networks, 2020, 26(3): 1-13.
8	张秋平, 孙胜, 刘敏, 等. 面向多边缘设备协作的任务卸载和服务缓存在线联合优化机制[J]. 计算机研究与发展, 2021, 58(6): 1318-1339.
	Zhang Qiu-ping, Sun Sheng, Liu Min, et al. Online joint optimization mechanism of task offloading and service caching for multi-edge device collaboration[J]. Journal of Computer Research and Development, 2021, 58(6): 1318-1339.
9	李燕君, 蒋华同, 高美惠. 基于强化学习的边缘计算网络资源在线分配方法[J]. 控制与决策, 2022, 37(11): 2880-2886.
	Li Yan-jun, Jiang Hua-tong, Gao Mei-hui. Reinforcement learning-based online resource allocation for edge computing network[J]. Control and Decision, 2022, 37(11): 2880-2886.
10	Lu H D, He X M, Du M, et al. Edge QOE: computation offloading with deep reinforcement learning for internet of things[J]. IEEE Internet of Things Journal, 2020, 7(10): 9255-9265.
11	韩旭. 基于优先级任务的电力物联网边缘计算任务卸载方法研究及实现[D]. 北京: 华北电力大学控制与计算机工程学院, 2022.
	Han Xu. Research and implementation of edge computing task offloading method for power internet of things based on priority task[D]. Beijing: School of Control and Computer Engineering,North China Electric Power University, 2022.
12	Adhikari M, Mukherjee M, Srirama S N. DPTO: a deadline and priority-aware task offloading in fog computing framework leveraging multilevel feedback queueing[J]. IEEE Internet of Things Journal, 2019, 7(7): 5773-5782.
13	赵海涛, 朱银阳, 丁仪, 等. 车联网中基于移动边缘计算的内容感知分类卸载算法研究[J]. 电子与信息学报, 2020, 42(1): 20-27.
	Zhao Hai-tao, Zhu Yin-yang, Ding Yi, et al. Research on content-aware classification offloading algorithm based on mobile edge calculation in the internet of vehicles[J]. Journal of Electronics & Information Technology, 2020, 42(1): 20-27.
14	Hu S H, Li G H. Dynamic request scheduling optimization in mobile edge computing for IOT applications[J]. IEEE Internet of Things Journal, 2020, 7(2): 1426-1437.
15	Lyu X C, Tian H, Jiang L, et al. Selective offloading in mobile edge computing for the green internet of things[J]. IEEE Network, 2018, 32(1): 54-60.
16	李智勇, 王琦, 陈一凡, 等. 车辆边缘计算环境下任务卸载研究综述[J]. 计算机学报, 2021, 44(5): 963-982.
	Li Zhi-yong, Wang Qi, Chen Yi-fan, et al. A survey on task offloading research in vehicular edge computing[J]. Chinese Journal of Computers, 2021, 44(5): 963-982.
17	Dai C, Wang Y P, Ye M. A new multi-objective particle swarm optimization algorithm based on decomposition[J]. Information Sciences, 2015, 325: 541-557.
18	Zapotecas M S, Garcia N A, Lopez J A. Multi-objective grey wolf optimizer based on decomposition[J]. Expert Systems with Applications, 2019, 120(4): 357-371.
19	Bi S Z, Huang L, Zhang Y J. Joint optimization of service caching placement and computation offloading in mobile edge computing systems[J]. IEEE Transactions on Wireless Communications, 2020, 19(7): 4947-4963.
20	张德干, 李霞, 张捷, 等. 基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法[J]. 电子与信息学报, 2022, 44(9): 3220-3230.
	Zhang De-gan, Li Xia, Zhang Jie, et al. New method of task offloading in mobile edge computing for vehicles based on simulated annealing[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3220-3230.
21	Mirjalili S, Saremi S, Mirjalili S M, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications, 2016, 47(5): 106-119.
22	Liang Z P, Wang X Y, Lin Q Z, et al. A novel multi-objective co-evolutionary algorithm based on decomposition approach[J]. Applied Soft Computing, 2018, 73(12): 50-66.
23	Wang J H, Zhang W W, Zhang J. Cooperative differential evolution with multiple populations for multiobjective optimization[J]. IEEE Transactions on Cybernetics, 2015, 46(12): 2848-2861.
24	Tian Y, Cheng R, Zhang X Y, et al. PlatEMO: a matlab platform for evolutionary multi-objective optimization educational forum[J]. IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87.
25	Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.

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