Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing

doi:10.1109/TMC.2024.3376550

科研成果详情

题名	Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing
作者	Xu，Changfu 1,2; Guo，Jianxiong3,4 ; Li，Yupeng 2; Zou，Haodong 1,2; Jia，Weijia 3,4; Wang，Tian3,4
发表日期	2024
发表期刊	IEEE Transactions on Mobile Computing
ISSN/eISSN	1536-1233
卷号	23 期号:11 页码:10523-10537
摘要	Collaborative Mobile Edge Computing (MEC) has emerged as a promising approach to provide low service latency for computation-intensive Internet of Things applications, facilitated by the cooperation of edge-edge and edge-cloud resources. However, existing collaborative MEC methods typically restrict the collaborative processing between any two Edge Servers (ESs) or one ES and the cloud server for a task request, limiting the exploitation of available resources on other ESs. Moreover, these conventional methods rely on offline task partitioning, potentially leading to extended make-span, especially when ES computing capacities exhibit heterogeneity. In this paper, we propose an innovative method named SMCoEdge. This method performs dynamic parallel multi-ES selection and workload allocation in heterogeneous collaborative MEC environments, thus simultaneously enabling multiple ESs' idle resources to accelerate task processing. We formulate our problem into an online linear programming problem, with the objective of minimizing task computing and transmission make-spans. To enhance computational efficiency, we decompose the problem into two stages: multi-ES selection and workload allocation. Then, we propose an online Deep Reinforcement Learning based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm along with a top-k//////k deep Q-learning network model to effectively solve our problem, where an efficient algorithm is proposed to achieve the optimal solution for the workload allocation stage. Furthermore, we provide a theoretical performance analysis, demonstrating that the DRL-SMO algorithm achieves a near-optimal solution for our problem within an approximate linear time complexity. Finally, our extensive experimental results demonstrate the substantial advantages of our method. It consistently reduces the average make-span by 19.63% and keeps a lower offloading failure rate, when compared to state-of-the-art methods. These findings underline the efficacy of our method in enhancing collaborative MEC performance.
关键词	Collaborative edge computing dynamic parallel multi-server selection and allocation edge-edge collaboration make-span optimization
DOI	10.1109/TMC.2024.3376550
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85188468431
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13750
专题	北师香港浸会大学
通讯作者	Wang，Tian
作者单位	1.BNU-HKBU United International College,Guangdong Provincial Key Laboratory IRADS,Zhuhai,519087,China 2.Hong Kong Baptist University,Hong Kong,999077,Hong Kong 3.Beijing Normal University,Institute of Artificial Intelligence and Future Networks,Zhuhai,519087,China 4.BNU-HKBU United International College,Guangdong Key Lab of AI and Multi-Modal Data Processing,Zhuhai,519087,China
第一作者单位	北师香港浸会大学
通讯作者单位	北师香港浸会大学
推荐引用方式 GB/T 7714	Xu，Changfu,Guo，Jianxiong,Li，Yupenget al. Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing[J]. IEEE Transactions on Mobile Computing, 2024, 23(11): 10523-10537.
APA	Xu，Changfu, Guo，Jianxiong, Li，Yupeng, Zou，Haodong, Jia，Weijia, & Wang，Tian. (2024). Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing. IEEE Transactions on Mobile Computing, 23(11), 10523-10537.
MLA	Xu，Changfu,et al."Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing". IEEE Transactions on Mobile Computing 23.11(2024): 10523-10537.