Path Selection for Seamless Service Migration in Vehicular Edge Computing

被引:24
|
作者
Xu, Jinliang [1 ]
Ma, Xiao [1 ]
Zhou, Ao [1 ]
Duan, Qiang [2 ]
Wang, Shangguang [1 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[2] Penn State Univ, Informat Sci & Technol Dept, Abington, PA 19001 USA
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Delays; Switches; Edge computing; 5G mobile communication; Quality of service; Internet of Things; Bandwidth; Mobile-edge computing (MEC); Pareto optimal; path selection; service migration; vehicular networks; MOBILITY; SCALE; USER;
D O I
10.1109/JIOT.2020.3000300
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile-edge computing provisions computing and storage resources by deploying edge servers (ESs) at the edge of the network to support ultralow delay and high bandwidth services. To ensure QoS of latency-sensitive services in vehicular networks, service migration is required to migrate data of the ongoing services to the closest ES seamlessly when users move across different ESs. To achieve seamless service migration, path selection is proposed to obtain one or more paths (consisting of several switches and ESs) to transfer service data. We focus on the following problems about path selection: 1) where to implement path selection? 2) how to coordinate interests of mobile users (i.e., vehicles) and network providers since they have conflicting interests during path selection? and 3) how to ensure seamless service migration during the migration of vehicles? To address the above problems, this article investigates path selection for seamless service migration. We propose a path-selection algorithm to jointly optimize both interests of the network plane (i.e., the cost for network providers) and service plane (i.e., QoE of users). We first formulate it as a multiobjective optimization problem and further prove theoretically that the proposed algorithm can give a weakly Pareto-optimal solution. Moreover, to improve the scalability of the proposed algorithm, a distance-based filter strategy is designed to eliminate undesired switches in advance. We conduct experiments on two synthesized data sets and the results validate the effectiveness of the proposed algorithm.
引用
收藏
页码:9040 / 9049
页数:10
相关论文
共 50 条
  • [1] A Novel Fault-Tolerant Approach for Dynamic Redundant Path Selection Service Migration in Vehicular Edge Computing
    Zhao, Jiale
    Ma, Yong
    Xia, Yunni
    Dai, Mengxuan
    Chen, Peng
    Long, Tingyan
    Shao, Shiyun
    Li, Fan
    Li, Yin
    Zeng, Feng
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (19):
  • [2] A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing
    Yuan, Quan
    Li, Jinglin
    Zhou, Haibo
    Lin, Tao
    Luo, Guiyang
    Shen, Xuemin
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (08) : 9041 - 9052
  • [3] Reservation Service: Trusted Relay Selection for Edge Computing Services in Vehicular Networks
    Hui, Yilong
    Su, Zhou
    Luan, Tom H.
    Li, Changle
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2020, 38 (12) : 2734 - 2746
  • [4] Predictive Migration Performance in Vehicular Edge Computing Environments
    Gilly, Katja
    Filiposka, Sonja
    Alcaraz, Salvador
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (03): : 1 - 16
  • [5] Service Migration in Mobile Edge Computing
    Wang, Shangguang
    Chou, Wu
    Wong, Kok-Seng
    Zhou, Ao
    Leung, Victor C.
    [J]. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2018,
  • [6] Dependency-Aware Service Migration for Backhaul-Free Vehicular Edge Computing Networks
    Fan, Qibing
    Chen, Li
    You, Changsheng
    Chen, Yunfei
    Yin, Huarui
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (01) : 1337 - 1352
  • [7] On Seamless Offloading of Delay Sensitive Vehicular Services over Mobile Edge Computing
    Labriji, Ibtissam
    Sesia, Stefania
    Perraud, Eric
    Strinati, Emilio Calvanese
    [J]. 2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2020,
  • [8] Efficient Edge Service Migration in Mobile Edge Computing
    Zeng, Zeng
    Li, Shihao
    Miao, Weiwei
    Wei, Lei
    Jiang, Chengling
    Wang, Chuanjun
    Zhang, Mingxuan
    [J]. 2020 IEEE 26TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2020, : 691 - 696
  • [9] Interference Aware Service Migration in Vehicular Fog Computing
    Ge, Shuxin
    Cheng, Meng
    Zhou, Xiaobo
    [J]. IEEE ACCESS, 2020, 8 : 84272 - 84281
  • [10] Task Migration Based on Reinforcement Learning in Vehicular Edge Computing
    Moon, Sungwon
    Park, Jaesung
    Lim, Yujin
    [J]. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2021, 2021