| Citation: | Haibo ZHANG, Yan CHENG, Kaijian LIU, Xiaofan HE. The Mobility Management Strategies by Integrating Mobile Edge Computing and CDN in Vehicular Networks[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1444-1451. doi: 10.11999/JEIT190571
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Due to the popularity of vehicle applications and the increase of the number of vehicles, the physical resources of roadside infrastructure are limited. When a large number of vehicles are connected to the vehicle networks, the energy consumption and latency are simultaneously increased. The framework for integrating the Content Delivery Network (CDN) and Mobile Edge Computing (MEC) can reduce the latency and energy consumption. In vehicle network, vehicle mobility poses a major challenge to the continuity of cloud services. Therefore, Mobility Management (MM) is proposed to deal with this problem. The Dynamic Channel Allocation algorithm with Overhead selection (ODCA) is used to avoid the ping-pong effect and reduces the handover time of vehicles between cells. The cooperative game algorithm based on RoadSide Unit (RSU) is used for virtual machine migration and a learning-based price control mechanism is developed to process vehicular computation resources efficiently. The simulation results show that the proposed algorithm can improve resource utilization and reduce overhead compared with the existing algorithms.
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ZHOU Siyu, NETALKAR P P, CHANG Yanan, et al. The MEC-based architecture design for low-latency and fast hand-off vehicular networking[C]. The 88th Vehicular Technology Conference, Chicago, USA, 2018: 1–7. doi: 10.1109/VTCFall.2018.8690790.
|
|
BASTUG E, BENNIS M, MEDARD M, et al. Toward interconnected virtual reality: Opportunities, challenges, and enablers[J]. IEEE Communications Magazine, 2017, 55(6): 110–117. doi: 10.1109/MCOM.2017.1601089
|
|
LIN Mengdan and ZHAO Xuelin. Application research of neural network in vehicle target recognition and classification[C]. 2019 International Conference on Intelligent Transportation, Big Data & Smart City, Changsha, China, 2019: 5–8. doi: 10.1109/ICITBS.2019.00010.
|
|
CICIRELLI F, GUERRIERI A, SPEZZANO G, et al. Edge computing and social internet of things for large-scale smart environments development[J]. IEEE Internet of Things Journal, 2018, 5(4): 2557–2571. doi: 10.1109/JIOT.2017.2775739
|
|
YALA L, FRANGOUDIS P A, and KSENTINI A. QoE-aware computing resource allocation for CDN-as-a-service provision[C]. 2016 IEEE Global Communications Conference, Washington, USA, 2016: 1–6. doi: 10.1109/GLOCOM.2016.7842182.
|
|
LIU Jiayi, YANG Qinghai, and SIMON G. Congestion avoidance and load balancing in content placement and request redirection for mobile CDN[J]. IEEE/ACM Transactions on Networking, 2018, 26(2): 851–863. doi: 10.1109/TNET.2018.2804979
|
|
XU Jie, SUN Yuxuan, CHEN Lixing, et al. E2M2: Energy efficient mobility management in dense small cells with mobile edge computing[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017: 1–6. doi: 10.1109/ICC.2017.7996855.
|
|
PENG Han, RAZI A, AFGHAH F, et al. A unified framework for joint mobility prediction and object profiling of drones in UAV networks[J]. Journal of Communications and Networks, 2018, 20(5): 434–442. doi: 10.1109/JCN.2018.000068
|
|
LIU Fangming, SHU Peng, and LUI J C S. AppATP: An energy conserving adaptive mobile-cloud transmission protocol[J]. IEEE Transactions on Computers, 2015, 64(11): 3051–3063. doi: 10.1109/TC.2015.2401032
|
|
XU Fei, LIU Fangming, LIU Linghui, et al. iAware: Making live migration of virtual machines interference-aware in the cloud[J]. IEEE Transactions on Computers, 2014, 63(12): 3012–3025. doi: 10.1109/TC.2013.185
|
|
CHEN Hongyang, WU Jianming, and SHIMOMURA T. New reference signal design for URLLC and eMBB multiplexing in new radio wireless communications[C]. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Bologna, Italy, 2018: 1220–1225. doi: 10.1109/PIMRC.2018.8580882.
|
|
KHAN Z, FAN Pingzhi, ABBAS F, et al. Two-level cluster based routing scheme for 5G V2X communication[J]. IEEE Access, 2019, 7: 16194–16205. doi: 10.1109/ACCESS.2019.2892180
|
|
WANG Chenmeng, YU F R, LIANG Chengchao, et al. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7432–7445. doi: 10.1109/TVT.2017.2672701
|
|
JOSHI G, VIG R, and SINGH S. DCA-based unimodal feature-level fusion of orthogonal moments for Indian sign language dataset[J]. IET Computer Vision, 2018, 12(5): 570–577. doi: 10.1049/iet-cvi.2017.0394
|
|
SUN Yuxuan, ZHOU Sheng, and XU Jie. EMM: Energy-aware mobility management for mobile edge computing in ultra dense networks[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(11): 2637–2646. doi: 10.1109/JSAC.2017.2760160
|
|
PLACHY J, BECVAR Z, and STRINATI E C. Dynamic resource allocation exploiting mobility prediction in mobile edge computing[C]. 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Valencia, Spain, 2016: 1–6. doi: 10.1109/PIMRC.2016.7794955.
|
|
CHEN Xiaojing, NI Wei, COLLINGS I B, et al. Automated function placement and online optimization of network functions virtualization[J]. IEEE Transactions on Communications, 2019, 67(2): 1225–1237. doi: 10.1109/TCOMM.2018.2877336
|
|
FRANGOUDIS P A and KSENTINI A. Service migration versus service replication in Multi-access Edge Computing[C]. 2018 14th International Wireless Communications & Mobile Computing Conference, Limassol, Cyprus, 2018: 124–129. doi: 10.1109/IWCMC.2018.8450284.
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