|Table of Contents|

Joint Task Offloading and Resource Allocation Method Based on Multi-Objective Optimization(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年01期
Page:
121-132
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Joint Task Offloading and Resource Allocation Method Based on Multi-Objective Optimization
Author(s):
Zhang Junna; Zhao Hao; Li Tianze; Zhao Xiaoyan; Wang Yali
(College of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China)
Keywords:
edge computing; task offloading; resource allocation; load balancing; reinforcement learning
PACS:
TP181
DOI:
10.3969/j.issn.1001-4616.2024.01.014
Abstract:
Offloading terminal tasks to the edge computing environment makes up for the large delay caused by the distance of cloud computing,and reduces the power consumption of equipment. But in terms of resources,all kinds of resources of edge server are not as sufficient as that of cloud server. Therefore,the joint optimization of task offloading and resource allocation becomes one of the research hotspots of edge computing. Existing studies on joint optimization of task offloading and resource allocation generally assume that tasks are offloading to a single edge server; By default,each terminal device generates one task. Even when multiple servers are considered,load balancing between servers is often ignored. Therefore,in a multi-edge server,multi-user and multi-task edge system,this paper proposes a task offloading and resource allocation method that balances delay,energy consumption and load balancing index(i.e.,benefit). By optimizing task offloading decision,server computing resource allocation and terminal device transmission power,the benefit of task offloading can be maximized. In order to verify the effectiveness of the proposed method,a full comparison experiment is carried out. The experimental results show that,compared with the comparison method,the proposed method has good performance in improving the unloading efficiency and realizing the load balancing between servers.

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Last Update: 2024-03-15