APMOVE: A Service Migration Technique for Connected and Autonomous Vehicles

被引:1
|
作者
Zakarya, Muhammad [1 ,2 ]
Gillam, Lee [3 ]
Ali Khan, Ayaz [4 ]
Rana, Omer [5 ]
Buyya, Rajkumar [6 ]
机构
[1] Sohar Univ, Fac CIT, Sohar 311, Oman
[2] Abdul Wali Khan Univ, Dept Comp Sci, Mardan 23200, Pakistan
[3] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, England
[4] Univ Lakki Marwat, Dept Comp Sci, Lakki Marwat 28420, Khyber Pakhtunk, Pakistan
[5] Univ Cardiff, Sch Comp Sci & Informat, Cardiff CF10 3AT, Wales
[6] Univ Melbourne, Sch Comp & Informat Syst, CLOUDS Lab, Melbourne, Vic 3052, Australia
来源
IEEE INTERNET OF THINGS JOURNAL | 2024年 / 11卷 / 17期
关键词
Cloud computing; Automobiles; Servers; Connected vehicles; Time factors; Internet of Things; Containers; edge cloud; service migration; EDGE; MANAGEMENT; INTERNET;
D O I
10.1109/JIOT.2024.3403415
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multiaccess edge computing systems (MECs) bring the capabilities of cloud computing closer to the radio access network (RAN), in the context of 4G and 5G telecommunication systems, and converge with existing radio access technologies like satellite or WiFi. An MEC is a cloud server that runs at the mobile network's edge and is installed and executed using virtual machines (VMs), containers, and/or functions. A cloudlet is similar to an MEC that consists of many servers which provide real-time, low-latency, computing services to connected users in close proximity. In connected vehicles, services may be provisioned from the cloud or edge that will be running users' applications. As a result, when users travel across many MECs, it will be necessary to transfer their applications in a transparent manner so that performance and connectivity are not negatively affected. In this article, we propose an effective strategy for migrating connected users' services from one edge to another or, more likely, to a remote cloud in an MEC. A mathematical model is presented to estimate the expected times to allocate and migrate services. Our evaluations, based on real workload traces and mobility patterns, suggest that the proposed strategy "ApMove" migrates connected services while ensuring their performance (similar to 0.004%-2.99% loss), reduced runtimes, therefore, users' costs (similar to 4.3%-11.63%), and minimizing the response time (similar to 7.45%-9.04%). Furthermore, approximately 17.39% migrations are avoided. We also study the impacts of variations in the car's speed and network transfer rates on service migration durations, latencies, and service execution times.
引用
收藏
页码:28721 / 28733
页数:13
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