Dynamic Service Migration and Request Routing for Microservice in Multicell Mobile-Edge Computing

Mobile-edge computing (MEC) sinks computation and storage capacities to network edge, where it is close to users to support delay-sensitive services. However, due to the dynamic and stochastic properties of MEC networks, the deployed services may be frequently migrated among edge servers to follow t...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-08, Vol.9 (15), p.13126-13143
Main Authors: Chen, Xiangyi, Bi, Yuanguo, Chen, Xueping, Zhao, Hai, Cheng, Nan, Li, Fuliang, Cheng, Wenlin
Format: Article
Language:English
Subjects:
Algorithms
Base stations
Cloud computing
Costs
Edge computing
Microbalances
Microservice architectures
Mobile computing
Mobile-edge computing (MEC)
Operating costs
Optimization
randomized rounding
request routing
Rounding
Routing
Servers
service migration
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Summary:Mobile-edge computing (MEC) sinks computation and storage capacities to network edge, where it is close to users to support delay-sensitive services. However, due to the dynamic and stochastic properties of MEC networks, the deployed services may be frequently migrated among edge servers to follow the mobility of users, which greatly increases the network operational cost. In this article, considering the service migration cost brought by user mobility, we study the joint optimization problem of service deployment and request routing decisions to maximize the long-term network utility of MEC networks. First, we propose a Lyapunov optimization-based online service migration algorithm to decompose the continuous optimization problem into a number of one-slot online optimization problems. Then, to address the NP-hard issue of one-slot optimization, we use a randomized rounding technique to implement service migration and request routing. Furthermore, through a closed-form theoretical analysis, we prove that the proposed algorithm not only greatly meets the local user requests and enables approximate performance guarantees but also adaptively balances the service migration cost and system performance online. Finally, extensive simulations are conducted, which demonstrate that our algorithm can efficiently utilize the storage and computation resources of edge servers, and maximize the long-term network utility while ensuring the stability of service migration cost.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3140183