Online Dependent Task Offloading by Application Partitioning in Edge Intelligence for Internet of Vehicles

doi:10.1109/JIOT.2024.3491854

科研成果详情

题名	Online Dependent Task Offloading by Application Partitioning in Edge Intelligence for Internet of Vehicles
作者	Wang，Wenhua 1,2,3; Zhang，Yilin 1; Zhang，Yutao 1; Liu，Qin 4; Wang，Tian1 ; Jia，Weijia 1
发表日期	2025
发表期刊	IEEE Internet of Things Journal
卷号	12 期号:5 页码:4860-4871
摘要	The Internet of Vehicles offers a comprehensive perception of environment, which enhance transportation efficiency. To handle the large amount of collected data, distributed edge intelligence is a promising paradigm in which the edge server share data and computing resources with each other, providing low-latency services for local devices. However, offloading the computing-intensive application fully to one edge server might lead to a large latency as the computing resource of edge servers are usually limited. To solve this problem and elevate Quality of Service (QoS) to new heights, existing methodologies merely partition applications into modules, overlooking the crucial fact that these modules harbor distinct input requirements, posing a pivotal challenge in scheduling optimization. In this article, we study dependent task offloading by partitioning applications and dividing modules into two categories: 1) stateful modules and 2) statelss modules. The stateful modules necessitate the incorporation of previous calculation results, while stateless modules operate independently. We subsequently frame this intricate dependent task offloading challenge as an optimization problem, boldly acknowledging its NP-hard nature. Considering this, we unveil an innovative online collaborative dependent task offloading (OCDTO) algorithm, grounded in a two-layer collaborative edge computing architecture. This algorithm meticulously minimizes the make-span, redefining the benchmarks for efficiency. Our rigorous experimentation not only validates but also showcases the superiority of our approach, consistently achieving the lowest average system cost compared to the state-of-the-art, which verifies the effectiveness of our proposed approach in latency-sensitive and computing-intensive scenarios.
关键词	Application partitioning deep reinforcement learning (DRL) edge intelligence task offloading
DOI	10.1109/JIOT.2024.3491854
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85209110196
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13732
专题	个人在本单位外知识产出
通讯作者	Zhang，Yilin; Zhang，Yutao
作者单位	1.Institute of Artificial Intelligence and Future Networks,Beijing Normal University,Zhuhai,519000,China 2.Shenzhen University,National Engineering Laboratory for Big Data System Computing Technology,Shenzhen,518060,China 3.Xi'an University of Posts and Telecommunications,Shaanxi Key Laboratory of Information Communication Network and Security,Xi'an,Shaanxi,710121,China 4.Hunan University,College of Computer Science and Electronic Engineering,Changsha,410082,China
推荐引用方式 GB/T 7714	Wang，Wenhua,Zhang，Yilin,Zhang，Yutaoet al. Online Dependent Task Offloading by Application Partitioning in Edge Intelligence for Internet of Vehicles[J]. IEEE Internet of Things Journal, 2025, 12(5): 4860-4871.
APA	Wang，Wenhua, Zhang，Yilin, Zhang，Yutao, Liu，Qin, Wang，Tian, & Jia，Weijia. (2025). Online Dependent Task Offloading by Application Partitioning in Edge Intelligence for Internet of Vehicles. IEEE Internet of Things Journal, 12(5), 4860-4871.
MLA	Wang，Wenhua,et al."Online Dependent Task Offloading by Application Partitioning in Edge Intelligence for Internet of Vehicles". IEEE Internet of Things Journal 12.5(2025): 4860-4871.