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

Abstract

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

CLC Number:

TP393.1

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.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Table 1

Parameter setting"

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

Table 1

Table 2

HV obtained by four algorithm schemes under different number of tasks"

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

Table 2

Table 3

HV obtained by four schemes under different number of edge servers"

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

Table 3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

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