Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios
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(2020) 2020:191
RESEARCH
Open Access
Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios Haowei Lin1† , Xiaolong Xu1*† *Correspondence: [email protected] † Xiaolong Xu and Haowei Lin contributed equally to this work. 1 Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, NanJing, China Full list of author information is available at the end of the article
, Juan Zhao2 and Xinheng Wang3
Abstract The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services’ migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services. Keywords: Dynamic service migration, Ultra-dense network, Multi-access edge computing, High-mobility scenarios
1 Introduction With the advent of various mobile and Internet of Things (IoT) devices, new types of services and mobile applications are emerging that utilize machine learning (ML) and augmented reality (AR) technology [1]. These services performed computing resources’ selection among MEC and centralized, cloud-based resources, towards efficient service orchestration [2]. They require high computing power and low latency due to recent advances in mobile network technology [3–7]. For example, the vehicular networks support more complex applications for both the vehicles and passengers nowadays, such as automatic driving, intelligent auxiliary driving for vehicles and augmented reality (AR), on-line interactive gaming, and other rich media applications for passengers [8], which require intensive communication and computation resources with low latency. Therefore, © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give approp
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