Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability
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影响因子：4.341
DOI码：10.1109/TCCN.2020.3018159
所属单位：School of ComputerScience and Information Engineering, Hefei University of Technology
发表刊物：IEEE Transactions on Cognitive Communications and Networking
项目来源：National Natural Science Foundation of China under Grants 61971176, and 61901156, Anhui Provincial N
关键字：Mobile edge computing, collaborative offloading, delivery rate
摘要：Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.
合写作者：Hao Zhou,Yibo Yi
第一作者：Caihong Kai
论文类型：期刊论文
通讯作者：Wei Huang
学科门类：工学
文献类型：J
卷号：7
期号：2
页面范围：624 - 634
ISSN号：2332-7731
是否译文：否
发表时间：2020-08-20
收录刊物：SCI、EI