車聯(lián)網(wǎng)中整合移動(dòng)邊緣計(jì)算與內(nèi)容分發(fā)網(wǎng)絡(luò)的移動(dòng)性管理策略
doi: 10.11999/JEIT190571 cstr: 32379.14.JEIT190571
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1.
重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065
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2.
武漢大學(xué)電子信息學(xué)院 武漢 430000
The Mobility Management Strategies by Integrating Mobile Edge Computing and CDN in Vehicular Networks
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1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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2.
School of Electronic Information, Wuhan University, Wuhan 430000, China
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摘要:
由于車載應(yīng)用的普及和車輛數(shù)量的增加，路邊基礎(chǔ)設(shè)施的物理資源有限，當(dāng)大量車輛接入車聯(lián)網(wǎng)時(shí)能耗與時(shí)延同時(shí)增加，通過整合內(nèi)容分發(fā)網(wǎng)絡(luò)(CDN)和移動(dòng)邊緣計(jì)算(MEC)的框架可以降低時(shí)延與能耗。在車聯(lián)網(wǎng)中，車輛移動(dòng)性對云服務(wù)的連續(xù)性提出了重大挑戰(zhàn)。因此，該文提出了移動(dòng)性管理(MM)來處理該問題。采用開銷選擇的動(dòng)態(tài)信道分配(ODCA)算法避免乒乓效應(yīng)且減少車輛在小區(qū)間的切換時(shí)間。采用基于路邊單元(RSU)調(diào)度的合作博弈算法進(jìn)行虛擬機(jī)遷移并開發(fā)基于學(xué)習(xí)的價(jià)格控制機(jī)制，以有效地處理MEC的計(jì)算資源。仿真結(jié)果表明，所提算法相比于現(xiàn)有的算法能夠提高資源利用率且減少開銷。
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關(guān)鍵詞:
- 車聯(lián)網(wǎng) /
- 移動(dòng)邊緣計(jì)算 /
- 內(nèi)容分發(fā)網(wǎng)絡(luò) /
- 小區(qū)間的切換 /
- 虛擬機(jī)遷移
Abstract: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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表 1 開銷選擇的動(dòng)態(tài)信道分配(ODCA)
(1) 輸入：${I_{{\rm{dd}}}}$, ${h_i}$, ${h_j}$, $v$, $c$, $\left( {{ X},{ Y}} \right)$ (2) 輸出：$\left( { {{ X}_{i,j} },{{ Y}_{i,j} } } \right)$, $t_{i,j}^{{\rm{h}} * }$, ${\rm{RS}}{{\rm{U}}^ * }$ (3) 初始化權(quán)值矩陣$\left( {{ X},{ Y}} \right)$ (4) $m \leftarrow 0$ (5) while $m \le {I_{ {\rm{dd} } } }$ (6) for $j = 1:M$ (7) L輛車同時(shí)進(jìn)行分布式計(jì)算，每個(gè)連接到RSU的V-UE僅報(bào)
告未定期使用信道的開銷(8) 如果許多用戶同時(shí)改變其信道，可能導(dǎo)致乒乓效應(yīng)，RSU
可通過${a_{i,j,\mathop l\limits^ {\wedge} } } = 1|\mathop l\limits^ {\wedge} = \max \dfrac{ { {p_{i,j,l} }{L_{i,j,l} } } }{ { {\sigma ^2} + {\rm{I} } } }$改變信道(9) 根據(jù)式(10)計(jì)算開銷，根據(jù)開銷最小來選擇最優(yōu)、次最優(yōu)、
次優(yōu)的3個(gè)RSU(10) V-UE實(shí)時(shí)上報(bào)其位置信息$\left( {{ X},{ Y}} \right)$和功率損耗門限${P^{{\rm{th}}}}$,
TCS根據(jù)式(5)計(jì)算切換位置$\left( { {{ X}_{i,j} },{{ Y}_{i,j} } } \right)$(11) 根據(jù)$\left( { {{ X}_{i,j} },{{ Y}_{i,j} } } \right)$和式(7)分別計(jì)算切換到3個(gè)RSU的時(shí)間，
如果能使$t_{i,j}^{\rm{C}}$和$t_{i,j}^{\rm{h}}$最小，此${\rm{RS}}{{\rm{U}}^ * }$性能最優(yōu)，且最優(yōu)切換
時(shí)間為$t_{i,j}^{{\rm{h}} * }$(12) endfor (13) endwhile 
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表 2 基于RSU調(diào)度的合作博弈算法
(1) 輸入：${{S}}$, $\alpha $, $\beta $, ${d_{i,j}}$, ${c_{i,j}}$, ${\rm{RS}}{{\rm{U}}^ * }$, ${I_{{\rm{dd}}}}$ (2) 輸出：${{A}}$ (3) 初始化權(quán)值矩陣${{A}}$,${{S}}$ (4)$m \leftarrow 0$ (5) while $m \le {I_{ {\rm{dd} } } }$ (6) for $j = 1:M$ (7) $L$輛車同時(shí)進(jìn)行分布式計(jì)算，利用梯度下降算法求出最優(yōu)功
率分配值$p_{i,j,l}^ * $(8) 根據(jù)式(14)判斷是否遷移 (9) 根據(jù)博弈論第2階段計(jì)算出未遷移與遷移的收益 (10) 根據(jù)行為${a_t}$觀察下一時(shí)刻的狀態(tài)${s_{t + 1}}$ (11) 根據(jù)式(16)—式(18)出獎(jiǎng)勵(lì)函數(shù)，通過不斷地學(xué)習(xí)，找到使
獎(jiǎng)勵(lì)函數(shù)最大的策略(12) endfor (13) endwhile
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表 3 模擬參數(shù)表
參數(shù) 數(shù)值 輸入數(shù)據(jù)的大小${d_{i,j}}$ 300～1600 kB 噪聲功率${\sigma ^2}$ 0.1～1.0 GHz MEC服務(wù)器CPU周期頻率${f^{\rm{C}}}$ 6 GHz 最大延遲容限${T^{{\rm{th}}}}$ 6 s 迭代次數(shù)${I_{{\rm{dd}}}}$ 600 最大傳輸功${P^{{\rm{max}}}}$ 23 dBm 傳輸帶寬$W$ 20 MHz 任務(wù)執(zhí)行時(shí)所需的CPU周期數(shù)${c_{i,j}}$ 0.1～1.0 GHz
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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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