A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Journal Artical > Item 310904400/115522

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115522

Title:	A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks
Authors:	Zha, Jianshan;Zhang, Jianshan;Li, Ming;Li, Ming;Zhe, Xianghan;Zheng, Xianghan;許慶賢;Hsu, Ching-Hsien
Contributors:	資訊電機學院資訊工程學系
Keywords:	cloudlet placement;computation offloading;mobile edge computing;wireless metropolitan area network;workflow application.
Date:	2022-04-01
Issue Date:	2023-03-29 02:30:00 (UTC+0)
Publisher:	亞洲大學
Abstract:	With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04-44.99%.
Appears in Collections:	[Department of Computer Science and Information Engineering] Journal Artical

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