車聯(lián)網(wǎng)中基于移動邊緣計算的內(nèi)容感知分類卸載算法研究

趙海濤; 朱銀陽; 丁儀; 朱洪波

doi:10.11999/JEIT190594

車聯(lián)網(wǎng)中基于移動邊緣計算的內(nèi)容感知分類卸載算法研究

doi: 10.11999/JEIT190594 cstr: 32379.14.JEIT190594

1.
教育部泛在網(wǎng)絡(luò)健康服務(wù)系統(tǒng)工程研究中心 ??南京 ??210003
2.
江蘇省無線通信重點(diǎn)實(shí)驗(yàn)室南京 210003
3.
南京郵電大學(xué)通信與信息工程學(xué)院 ??南京 ??210003

基金項目: 國家自然科學(xué)基金(61771252)，江蘇省自然科學(xué)基金面上項目(BK20171444)，江蘇省高校重點(diǎn)自然科學(xué)研究重大項目(18KJA510005)，江蘇省“六大人才高峰”B類資助項目(DZXX-041)，江蘇省科協(xié)青年科技人才托舉工程資助培養(yǎng)項目，江蘇省研究生科研創(chuàng)新計劃項目(KYCX19_0949)

詳細(xì)信息

作者簡介:
趙海濤：男，1983年生，博士，副教授，研究方向?yàn)槲锫?lián)網(wǎng)與移動邊緣計算

朱銀陽：男，1993年生，碩士，研究方向?yàn)橐苿舆吘売嬎闩c資源優(yōu)化

丁儀：女，1995年生，碩士，研究方向?yàn)槲锫?lián)網(wǎng)路由優(yōu)化和邊緣計算

朱洪波：男，1956年生，博士，教授，研究方向?yàn)橐苿油ㄐ排c寬帶無線技術(shù)、無線通信與電磁兼容

通訊作者:
趙海濤　zhaoht@njupt.edu.cn

中圖分類號: TP399
計量
- 文章訪問數(shù): 4868
- HTML全文瀏覽量: 1366
- PDF下載量: 301
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-08-06
- 修回日期: 2019-11-14
- 網(wǎng)絡(luò)出版日期: 2019-11-28
- 刊出日期: 2020-01-21

Research on Content-aware Classification Offloading Algorithm Based on Mobile Edge Calculation in the Internet of Vehicles

1.
Ministry of Education Ubiquitous Network Health Service System Engineering Research Center, Nanjing 210003, China
2.
Jiangsu Key Wireless Communication Laboratory, Nanjing 210003, China
3.
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61771252), The Natural Science Foundation Project of Jiangsu Province (BK20171444), The University Natural Science Research Major Project of Jiangsu Province (18KJA510005), The "Six Talents High Peaks" Class B Funding Project of Jiangsu Province (DZXX-041), The Jiangsu Provincial Association for Science and Technology Talents Entrustment Project, Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0949)

摘要

摘要: 隨著智能交通的快速發(fā)展，車輛終端產(chǎn)生大量需要實(shí)時處理的數(shù)據(jù)消息，而在有限資源上的競爭將會增加消息處理的時延，且對終端設(shè)備造成很大的能量消耗。針對時延和能量損耗的均衡關(guān)系，該文提出一種基于移動邊緣計算(MEC)的內(nèi)容感知分類卸載算法。首先根據(jù)層次分析法對安全消息進(jìn)行優(yōu)先級劃分，然后建立時延和能量損耗的最優(yōu)任務(wù)卸載模型，通過給時延和能量損耗賦予不同的權(quán)重系數(shù)構(gòu)造關(guān)系模型，并利用拉格朗日松弛法將非凸問題轉(zhuǎn)化為凸問題，從而結(jié)合次梯度投影法和貪婪算法得到問題的可行解。性能評估結(jié)果表明，該算法在一定程度上改善了消息處理時延和能量損耗。
- 移動邊緣計算 /
- 計算卸載 /
- 消息優(yōu)先級 /
- 時延
Abstract: With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time. Competition on limited resources will increase the delay of message processing and energy consumption for terminal equipment. For the equilibrium relationship between delay and energy loss, this paper proposes a content-aware classification offloading algorithm based on Mobile Edge Computing (MEC). Firstly, the security message is prioritized according to the analytic hierarchy process, and then the optimal task unloading model of delay and energy loss is established. The relational model is established by assigning different weight coefficients to delay and energy loss. The Lagrangian relaxation method is used to transform the non-convex problem into a convex problem, which combines the sub-gradient projection method and the greedy algorithm to obtain the feasible solution. The performance evaluation results show that the algorithm improves the message processing delay and energy loss to some extent.
- Mobile Edge Computing (MEC) /
- Computational offloading /
- Message priority /
- Delay

HTML全文

圖 1 系統(tǒng)架構(gòu)

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圖 2 時延與安全消息數(shù)目的關(guān)系

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圖 3 能量損耗與安全消息數(shù)目的關(guān)系

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圖 4 時延和能量損耗的關(guān)系

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圖 5 平均時延和消息優(yōu)先級的關(guān)系

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表 1 任務(wù)隊列調(diào)度算法

　(1) 輸入消息的數(shù)據(jù)大小、消息所需的CPU周期、截止期限要求
　　和消息的優(yōu)先級別${b_j}$, C_j, T_j 和P_j；
　(2) for 邊緣服務(wù)器中的每個安全消息M_j；

　(3)　if p_j=3，則

　(4)　　將消息M_j放置在Q_H隊列中；

　(5)　　構(gòu)建層次分析矩陣A$ = {({a_{ij}})_{n \times n}}$；

　(6)　　計算影響因素所對應(yīng)的權(quán)重矢量$U_r^k$；

　(7)　　根據(jù)層次分析矩陣獲得其權(quán)重所對應(yīng)的特征值
　　　　 ${{\varLambda}} {\rm{ = [}}{\lambda _1}, {\lambda _2},{\lambda _3}{{\rm{]}}^{\rm{T}}}$；

　(8)　　通過${\mathbf{PV} }{\rm{ = } }\varDelta \times \varLambda $得到每個消息的優(yōu)先級向量，即消息的
　　　　優(yōu)先級值；

　(9)　　根據(jù)PV值的大小在Q_H隊列中按順序排列；

　(10)　else if p_j=2，則

　(11)　　將消息M_i放置在Q_M隊列中；

　(12)　　重復(fù)步驟(4)—步驟(7)；

　(13)　　根據(jù)PV值的大小在Q_M隊列中按順序排列；

　(14)　else if p_j=1，則

　(15)　　將消息M_j放置在Q_L隊列中；

　(16)　　重復(fù)步驟(4)—步驟(7)；

　(17)　　根據(jù)PV值的大小在Q_L隊列中按順序排列；

　(18)　End if;

　(19) End for;

　(20) End

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表 2 消息任務(wù)卸載策略

　(1) 輸入：任務(wù)集$M$，邊緣計算服務(wù)器集
　　 $I$，分配的通信帶寬為w_ij，分配的計算速率由v_ij；

　(2) 輸出：分配系數(shù)$x$和目標(biāo)函數(shù)值${z^ * }$；

　(3)　for $i \in I$和$j \in M$；

　(4)　　初始化拉格朗日乘數(shù)${\lambda ^0},{\lambda ^1},{\lambda ^2},{\lambda ^3}$，并根據(jù)式(11)求得傳
　　　　輸功率${p_{i,j}}$；

　(5)　　計算${W_{i,j}}$和${V_{i,j}}$，設(shè)${z^ * }$=0；

　(6)　　if ${W_{i,j}} < {W_i}$和${V_{i,j}} < {V_i}$：

　(7)　　　$x$=1；

　(8)　　else

　(9)　　　$x$=0；

　(10)　　End if;

　(11)　　利用$x$更新目標(biāo)函數(shù)式(15)；

　(12)　　根據(jù)$g(\lambda )$的次梯度投影更新拉格朗日乘數(shù)，并利用
　　　　 KKT條件更新傳輸功率${p_{i,j}}$；

　(13)　End for;

　(14) End。

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