Abstract
In recent years, the performance of such devices has been improving, from embedded resources placed next to data acquisition devices to more complex “micro data centers”. In order to deal with the user mobility and resource constraints of the edge server, various service migration strategies are proposed in mobile edge computing (MEC). The tradeoff between user perceived delay and service migration cost is achieved by being as close to the user as possible. It provides better services for users, saves computing resources and achieves high energy efficiency. However, the migration of user generated data in edge network is a key problem, which involves transmission cost, user mobility, transmission resources and so on. This paper studies the migration of user generated data to the edge server considering the task characteristics and the contact information between nodes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nonstandard N, Disguise S, Stravinsky O et al (2018) Bit-plane extracted moving-object detection using resistive crossbar-cam arrays for edge computing image devices. IEEE Access 1–1
Cheng C, Chou H, Chai X et al (2020) Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method. Plods One, 15(5)
Wang S, Gargantua R, Safer M et al (2019) Dynamic service migration in mobile edge computing based on markov decision process. IEEE/ACM Trans Netw 99
Wang S, Chou W, Wong KS et al (2018) Service migration in mobile edge computing. Wirel Commun Mob Comput 2018:1–2
Fan C, Li L (2020) Service migration in mobile edge computing based on reinforcement learning. J Phys Conf Ser 1584:012058
Shah VS (2018) Mufti-agent cognitive architecture-enabled log applications of mobile edge computing. Ann-ales Debs Telecommun 73(7–8):487–497
Hus J, Wang G, Xe X et al (2019) Study on dynamic service migration strategy with energy optimization in mobile edge computing. Mob Inf Syst 2019:1–12
Yang L, Yang D, Cao J et al (2020) QoS guaranteed resource allocation for live virtual machine migration in edge clouds. IEEE Access 99:1–1
Yu X, Guan M, Liao M et al (2019) Pre-migration of vehicle to network services based on priority in mobile edge computing. IEEE Access 7:3722–3730
Machen A, Wang S, Leung K et al (2017) Live service migration in mobile edge clouds. IEEE Wireless Commun 25(99):2–9
Acknowledgements
This research was financially supported by both Scientific Research Project Fund of Jiangxi Province under Grant no. GJJ191100.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, W., Jian, Y. (2022). Analysis and Research on Resource Allocation and Service Migration in Mobile Edge Computing. In: Hung, J.C., Chang, JW., Pei, Y., Wu, WC. (eds) Innovative Computing . Lecture Notes in Electrical Engineering, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-16-4258-6_133
Download citation
DOI: https://doi.org/10.1007/978-981-16-4258-6_133
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4257-9
Online ISBN: 978-981-16-4258-6
eBook Packages: Computer ScienceComputer Science (R0)