Reducing the network overhead of user mobility–induced virtual machine migration in mobile edge computing. (28th September 2018)

• Record Type: Journal Article
• Authors: Zhang, Fei; Liu, Guangming; Zhao, Bo; Fu, Xiaoming; Yahyapour, Ramin
• Other Names: Somani Gaurav guestEditor.; Zhao Xinghui guestEditor.; Srirama Satish Narayana guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 49:Number 4(2019)
• Page Start: 673
• Page End: 693
• Publication Date: 2018-09-28
• Subjects: Internet of Things -- live migration -- mobile edge computing -- network overhead -- user mobility -- virtual machine; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2642
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms ( M‐All and M‐Edge ), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight‐based algorithm ( M‐Weight ) and a mobility prediction–based heuristic algorithm ( M‐Predict ) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.