基于移動路徑預測的車載邊緣計算卸載切換策略研究
doi: 10.11999/JEIT190483 cstr: 32379.14.JEIT190483
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云南大學信息學院 昆明 650500
基金項目: 國家自然科學基金(61562092)，云南大學信息學院研究生科研創(chuàng)新項目(Y2000211)
Mobility Prediction Based Computation Offloading Handoff Strategy for Vehicular Edge Computing
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School of Information Science and Engineering, Yunnan University, Kunming 650500, China
Funds: The National Natural Science Foundation of China (61562092), The Graduate Research and Innovation Project of Yunnan University (Y2000211)
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摘要: 車載云計算環(huán)境中的計算卸載存在回程網(wǎng)絡延遲高、遠程云端負載大等問題，車載邊緣計算利用邊緣服務器靠近車載終端，就近提供云計算服務的特點，在一定程度上解決了上述問題。但由于汽車運動造成的通信環(huán)境動態(tài)變化進而導致任務完成時間增加，為此該文提出一種基于移動路徑可預測的計算卸載切換策略MPOHS，即在車輛移動路徑可預測情況下，引入基于最小完成時間的計算切換策略，以降低車輛移動性對計算卸載的影響。實驗結(jié)果表明，相對于現(xiàn)有研究，該文所提算法能夠在減少平均任務完成時間的同時，減少切換次數(shù)和切換時間開銷，有效降低汽車運動對計算卸載的影響。Abstract: In the vehicular cloud computing environments, computation offloading faces the problems such as high network delay and large load of the remote cloud. The vehicular edge computing takes advantage of the edge servers to be close to the vehicular terminals, and provides the cloud computing service to solve the problem mentioned above. However, due to the dynamic change of communication environment caused by vehicle movement, the task completion time will increase. For this reason, this paper proposes a Mobility Prediction-based computation Offloading Handoff Strategy (MPOHS), which tries to minimize the average completion time of offloaded tasks by migrating tasks among edge servers according to the prediction of vehicle movement. The experimental results show that, compared with the existing research, the proposed strategy can reduce the average task completion time, cut down the handoff times and handoff time overhead, and effectively reduce the impact of vehicle movement on the performance of computation offloading.
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表 1 仿真參數(shù)表
參數(shù)名 參數(shù)值 參數(shù)名 參數(shù)值 基站覆蓋范圍 100% V計算能力(MIPS) 82335×0.75 RSU半徑(m) U[100 150] ES計算能力(MIPS) 82335×1.5 Cloud數(shù)目(個) 1 Cloud計算能力(MIPS) 82335×2 BS數(shù)目(個) 1 V2R單跳帶寬(Mbps) 15 RSU數(shù)目(個) 64 V2B單跳帶寬(Mbps) 2 ES數(shù)目(個) 16 E2E單跳帶寬(Mbps) U[15 20] V數(shù)目(個) 10 V2V端到端延時(ms) U[5 15] 任務計算量(MI) U[7 9]×106 V2R端到端延時(ms) U[20 30] 任務數(shù)據(jù)量(M) U[30 50] V2B端到端延時(ms) U[450 550] 
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MACH P and BECVAR Z. Mobile edge computing: A survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628–1656. doi: 10.1109/COMST.2017.2682318 TRAN T X, HAJISAMI A, PANDEY P et al. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges[J]. IEEE Communications Magazine, 2017, 55(4): 54–61. doi: 10.1109/MCOM.2017.1600863 張海波, 李虎, 陳善學, 等. 超密集網(wǎng)絡中基于移動邊緣計算的任務卸載和資源優(yōu)化[J]. 電子與信息學報, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592ZHANG Haibo, LI Hu, CHEN Shanxue, et al. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592 張海波, 欒秋季, 朱江, 等. 基于移動邊緣計算的V2X任務卸載方案[J]. 電子與信息學報, 2018, 40(11): 2736–2743. doi: 10.11999/JEIT180027ZHANG Haibo, LUAN Qiuji, ZHU Jiang, et al. V2X task offloading scheme based on mobile edge computing[J]. Journal of Electronics &Information Technology, 2018, 40(11): 2736–2743. doi: 10.11999/JEIT180027 ZHANG Ke, MAO Yuming, LENG Supeng, et al. Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading[J]. IEEE Vehicular Technology Magazine, 2017, 12(2): 36–44. doi: 10.1109/MVT.2017.2668838 AISSIOUI A, KSENTINI A, GUEROUI A M et al. On enabling 5G automotive systems using follow me edge-cloud concept[J]. IEEE Transactions on Vehicular Technology, 2018, 67(6): 5302–5316. doi: 10.1109/TVT.2018.2805369 NING Zhaolong, WANG Xiaojie, and HUANG Jun. Mobile edge computing-enabled 5G vehicular networks: Toward the integration of communication and computing[J]. IEEE Vehicular Technology Magazine, 2019, 14(1): 54–61. doi: 10.1109/MVT.2018.2882873 CHEN Hongyang, GAO Feifei, MARTINS M, et al. Accurate and efficient node localization for mobile sensor networks[J]. Mobile Networks and Applications, 2013, 18(1): 141–147. doi: 10.1007/s11036-012-0361-7 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 MATHEW T, SEKARAN K C, and JOSE J. Study and analysis of various task scheduling algorithms in the cloud computing environment[C]. 2014 International Conference on Advances in Computing, Communications and Informatics, New Delhi, India, 2014: 658–664. CHEN Hongyang, WU Jianming, and SHIMOMURA T. New reference signal design for URLLC and eMBB multiplexing in new radio wireless communications[C]. The 29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Bologna, Italy, 2018: 1220–1225. LI Bo, PEI Yijian, WU Hao, et al. Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds[J]. The Journal of Supercomputing, 2015, 71(8): 3009–3036. doi: 10.1007/s11227-015-1425-9 李波, 黃鑫, 牛力, 等. 車載邊緣計算環(huán)境中的任務卸載決策和優(yōu)化[J]. 微電子學與計算機, 2019, 36(2): 78–82.LI Bo, HUANG Xin, NIU Li, et al. Task offloading decision in vehicle edge computing environment[J]. Microelectronics &Computer, 2019, 36(2): 78–82. XIAO Kaiyi and LI Changgen. Vertical handoff decision algorithm for heterogeneous wireless networks based on entropy and improved TOPSIS[C]. The 18th IEEE International Conference on Communication Technology, Chongqing, China, 2018: 706–710. MA Lele, YI Shanhe, and LI Qun. Efficient service handoff across edge servers via docker container migration[C]. The 2nd ACM/IEEE Symposium on Edge Computing, San Jose, USA, 2017: 1–13. 郭麗芳, 李鴻燕, 李艷萍, 等. 無線Ad Hoc網(wǎng)絡移動模型大全[M]. 北京: 人民郵電出版社, 2014.GUO Lifang, LI Hongyan, LI Yanping, et al. The Encyclopedia of Wireless Ad Hoc Network Mobility Model[M]. Beijing: The People’s Posts and Telecommunications Press, 2014. -
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