Paper Info
Title
Mobility Aware and Dynamic Migration of MEC Services for the Internet of Vehicles
Abstract
Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs.
Year
DOI
Venue
2021
10.1109/TNSM.2021.3052808
IEEE Transactions on Network and Service Management
Keywords
DocType
Volume
5G,Internet of Vehicles (IoV),multi-access edge computing (MEC),virtual machine (VM),service migration,mobility estimation,Lyapunov optimization,recurrent neural network,convolutional neural network,Markov chain
Journal
18
Issue
ISSN
Citations 
1
1932-4537
6
PageRank 
References 
Authors
0.44
0
7
Name
Order
Citations
PageRank
Ibtissam Labriji160.78
Francesca Meneghello2301.98
Davide Cecchinato360.44
Stefania Sesia460.44
Eric Perraud560.44
Emilio Calvanese Strinati631135.74
Michele Rossi722826.33