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融合區(qū)塊鏈與霧計算系統(tǒng)中基于網(wǎng)絡(luò)時延和資源管理的優(yōu)化任務(wù)卸載方案

劉通 唐倫 何小強(qiáng) 陳前斌

劉通, 唐倫, 何小強(qiáng), 陳前斌. 融合區(qū)塊鏈與霧計算系統(tǒng)中基于網(wǎng)絡(luò)時延和資源管理的優(yōu)化任務(wù)卸載方案[J]. 電子與信息學(xué)報, 2020, 42(9): 2180-2185. doi: 10.11999/JEIT190654
引用本文: 劉通, 唐倫, 何小強(qiáng), 陳前斌. 融合區(qū)塊鏈與霧計算系統(tǒng)中基于網(wǎng)絡(luò)時延和資源管理的優(yōu)化任務(wù)卸載方案[J]. 電子與信息學(xué)報, 2020, 42(9): 2180-2185. doi: 10.11999/JEIT190654
Tong LIU, Lun TANG, Xiaoqiang HE, Qianbin CHEN. Optimal Task Offloading Scheme Based on Network Delay and Resource Management in Joint Blockchain and Fog Computing System[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2180-2185. doi: 10.11999/JEIT190654
Citation: Tong LIU, Lun TANG, Xiaoqiang HE, Qianbin CHEN. Optimal Task Offloading Scheme Based on Network Delay and Resource Management in Joint Blockchain and Fog Computing System[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2180-2185. doi: 10.11999/JEIT190654

融合區(qū)塊鏈與霧計算系統(tǒng)中基于網(wǎng)絡(luò)時延和資源管理的優(yōu)化任務(wù)卸載方案

doi: 10.11999/JEIT190654
基金項(xiàng)目: 國家自然科學(xué)基金(61571073),重慶市教委科學(xué)技術(shù)研究重大項(xiàng)目(KJZD-M201800601)
詳細(xì)信息
    作者簡介:

    劉通:男,1985年生,博士生,高級工程師,研究方向?yàn)檫吘売嬎恪^(qū)塊鏈等

    唐倫:男,1973年生,博士,教授,博士生導(dǎo)師,研究方向?yàn)樾乱淮鸁o線通信網(wǎng)絡(luò)、異構(gòu)蜂窩網(wǎng)絡(luò)、軟件定義無線網(wǎng)絡(luò)等

    何小強(qiáng):男,1992年生,博士生,研究方向?yàn)?G網(wǎng)絡(luò)切片、邊緣計算等

    陳前斌:男,1967年生,博士,教授,博士生導(dǎo)師,研究方向?yàn)閭€人通信、多媒體信息處理與傳輸、下一代移動通信網(wǎng)絡(luò)等

    通訊作者:

    劉通 liuyyshiwo@163.com

  • 中圖分類號: TN929.5

Optimal Task Offloading Scheme Based on Network Delay and Resource Management in Joint Blockchain and Fog Computing System

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)
  • 摘要: 針對如何基于有限的系統(tǒng)剩余資源進(jìn)行任務(wù)優(yōu)化卸載以增加移動終端的數(shù)字貨幣收益問題,該文在融合區(qū)塊鏈與霧計算系統(tǒng)中提出一種基于節(jié)點(diǎn)剩余資源、網(wǎng)絡(luò)時延的任務(wù)卸載方案。為了實(shí)現(xiàn)任務(wù)的優(yōu)化卸載,首先基于任務(wù)量對移動終端的預(yù)期收益進(jìn)行了分析,其次基于網(wǎng)絡(luò)節(jié)點(diǎn)剩余計算資源、存儲資源、功率資源、網(wǎng)絡(luò)時延聯(lián)合分析了移動終端的支出。此后以最大化移動終端的數(shù)字貨幣收益為優(yōu)化目標(biāo)建立了數(shù)學(xué)優(yōu)化模型,并利用模擬退火(SA)算法對優(yōu)化模型進(jìn)行求解。仿真結(jié)果證明上述方案的有效性。
  • 圖  1  系統(tǒng)模型

    圖  2  節(jié)點(diǎn)數(shù)量與時延的關(guān)系

    圖  3  不同資源類型與定價的關(guān)系

    圖  4  將任務(wù)遷移至不同執(zhí)行主體的開銷對比

    圖  5  將任務(wù)遷移至不同執(zhí)行主體的收益對比

    圖  6  使用不同算法時的MUB收益對比

    圖  7  數(shù)據(jù)量大小與收益的關(guān)系

    表  1  求解優(yōu)化模型的模擬退火算法流程

     輸入:${Q_{\rm{n}}}$, $\varphi $, ${T_{\max }}$, ${T_{\min }}$, $L$, $\nu $;
     輸出:優(yōu)化解$\pi^* = \{ \alpha^*,\beta^*\}$;
     (1) 初始化${Q_{\rm{n}}}$, 最高溫度${T_{\max }}$和最低溫度${T_{\min }}$,降溫速度$\nu $,迭代
       次數(shù)$L$;
     (2) for m=1:L do;
     (3) 求解狀態(tài)${\pi _m} = \{ {\alpha _m},{\beta _m}\}$, 并計算當(dāng)前$E_{{\rm{n}}m}^{\rm{T}}$,將
       ${\pi _m} = \{ {\alpha _m},{\beta _m}\}$賦值給最佳解$\pi^* = \{ \alpha^*,\beta^*\}$;
     (4) 計算下一狀態(tài)解${\pi '} = \{ {\alpha '},{\beta '}\}$,并計算對應(yīng)的$E_{{\rm{n}}(m + 1)}^{\rm{T}}$;
     (5) 計算增量$\Delta E_{{\rm{n}}m}^{\rm{T}} = E_{{\rm{n}}(m + 1)}^{\rm{T}} - E_{{\rm{n}}m}^{\rm{T}}$;
     (6)  if $\Delta E_{{\rm{n}}m}^{\rm{T}} < 0$ then;
     (7) 接受當(dāng)前解為最佳解,并將${\pi _m} = \{ {\alpha _m},{\beta _m}\} $賦值給最佳解
       $\pi^* = \{ \alpha^*,\beta^*\}$;
     (8)  else;
     (9) 以metropolis準(zhǔn)則中的概率${p_{{\rm{sol}}}}$接受${\pi _m} = \{ {\alpha _m},{\beta _m}\}$作為當(dāng)
       前最佳解;
     (10)  End if;
     (11)  if m≠L;
     (12)   返回(2);
     (13)  else;
     (14)    end for;
     (15) $T(k + 1) = \nu T(k)$;
     (16) if $T(k + 1) > {T_{\min }}$;
     (17)   返回(2);
     (18) else;
     (19) end for
    下載: 導(dǎo)出CSV

    表  2  各種節(jié)點(diǎn)的總資源和剩余資源量及其權(quán)重的設(shè)置

    節(jié)點(diǎn)類型資源類型總資源剩余資源權(quán)重
    BN存儲資源(GB)1024[600, 800]0.33
    計算資源(MCPS)1000[600, 800]0.33
    功率資源(W)400[280, 350]0.34
    FN存儲資源(GB)512[256, 300]0.33
    計算資源(MCPS)400[280, 350]0.33
    功率資源(W)40[28, 30]0.34
    MN存儲資源(GB)128[16, 32]0.33
    計算資源(MCPS)200[100, 160]0.33
    功率資源(W)2[1.2, 1.6]0.34
    下載: 導(dǎo)出CSV
  • BACCARELLI E, NARANJO P G V, SCARPINITI M, et al. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study[J]. IEEE Access, 2017, 5: 9882–9910. doi: 10.1109/ACCESS.2017.2702013
    張海波, 李虎, 陳善學(xué), 等. 超密集網(wǎng)絡(luò)中基于移動邊緣計算的任務(wù)卸載和資源優(yōu)化[J]. 電子與信息學(xué)報, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592

    ZHANG 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
    CHEN Lixing, ZHOU Pan, GAO Liang, et al. Adaptive fog configuration for the industrial internet of things[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4656–4664. doi: 10.1109/TⅡ.2018.2846549
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    LIU Mengting, YU F R, TENG Yinglei, et al. Computation offloading and content caching in wireless blockchain networks with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 11008–11021. doi: 10.1109/TVT.2018.2866365
    XIONG Zehui, ZHANG Yang, NIYATO D, et al. When mobile blockchain meets edge computing[J]. IEEE Communications Magazine, 2018, 56(8): 33–39. doi: 10.1109/MCOM.2018.1701095
    YAO Haipeng, MAI Tianle, WANG Jingjing, et al. Resource trading in blockchain-based industrial internet of things[J]. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3602–3609. doi: 10.1109/TⅡ.2019.2902563
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出版歷程
  • 收稿日期:  2019-08-29
  • 修回日期:  2019-10-25
  • 網(wǎng)絡(luò)出版日期:  2020-03-18
  • 刊出日期:  2020-09-27

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