基于Lyapunov優(yōu)化的隱私感知計(jì)算卸載方法
doi: 10.11999/JEIT190170
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國(guó)家數(shù)字交換系統(tǒng)工程技術(shù)研究中心 鄭州 450002
A Privacy-aware Computation Offloading Method Based on Lyapunov Optimization
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National Digital Switching System Engineering R&D Center, Zhengzhou 450002, China
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摘要:
移動(dòng)邊緣計(jì)算(MEC)中計(jì)算卸載決策可能暴露用戶特征，導(dǎo)致用戶被鎖定。針對(duì)此問(wèn)題，該文提出一種基于Lyapunov優(yōu)化的隱私感知計(jì)算卸載方法。首先，該方法定義卸載任務(wù)中的隱私量，并引入隱私限制使各MEC節(jié)點(diǎn)上卸載任務(wù)的累積隱私量盡可能??；然后，提出假任務(wù)機(jī)制權(quán)衡終端能耗和隱私保護(hù)的關(guān)系，當(dāng)系統(tǒng)因隱私限制無(wú)法正常執(zhí)行計(jì)算卸載時(shí)，在MEC節(jié)點(diǎn)生成虛假的卸載任務(wù)以降低累積隱私量；最后，建立隱私感知計(jì)算卸載模型，并基于Lyapunov優(yōu)化原理求解。仿真結(jié)果表明，基于Lyapunov優(yōu)化的隱私感知卸載算法(LPOA)能使用戶的累積隱私量穩(wěn)定在0附近，且總卸載頻率與不考慮隱私的決策一致，有效保護(hù)了用戶隱私，同時(shí)保持了較低的平均能耗。
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關(guān)鍵詞:
- 移動(dòng)邊緣計(jì)算 /
- 計(jì)算卸載 /
- 卸載決策 /
- 隱私保護(hù) /
- Lyapunov優(yōu)化
Abstract:The decision on computation offloading to Mobile Edge Computing (MEC) may expose user’s characteristics and cause the user to be locked. A privacy-aware computation offloading method based on Lyapunov optimization is proposed in this paper. Firstly, the privacy of task is defined, and privacy restrictions are introduced to minimize the cumulative privacy of each MEC node; Then, the fake task mechanism is proposed to balance the terminal energy consumption and privacy protection, reducing the cumulative privacy of MEC node by generating a fake task non-feature task when offloading is not performed due to privacy restrictions; Finally, the privacy-aware computing offloading decision is modeled and solved based on the Lyapunov optimization. Simulation results validate that the Lyapunov optimization-based Privacy-aware Offloading Algorithm (LPOA) can stabilize user’s privacy near zero, and the total offloading frequency is consistent with the decision that don’t consider privacy, effectively protecting user’s privacy while maintaining a low average energy consumption.
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表 1 LPOA
初始化：設(shè)置各MEC節(jié)點(diǎn)的累積隱私量$Q{\rm{(}}t{\rm{) = 0}}$ (1) For t=1,2, ···,T Do (2) 觀察當(dāng)前無(wú)線信道增益${\rm{\{ }}h_k^2{\rm{(}}t{\rm{)\} }}_{k = 1}^{{N_{{\rm{MEC}}}}}$和任務(wù)截止時(shí)間$\xi (t)$； (3) 根據(jù)策略1計(jì)算${f^*}{\rm{(}}t{\rm{)}},E_{\rm{L}}^*{\rm{(}}t{\rm{)}},\left[ {p_k^*{\rm{(}}t{\rm{)}},E_k^*{\rm{(}}t{\rm{)}}} \right]_{k = 1}^{{N_{{\rm{MEC}}}}}$； (4) 根據(jù)式(9)獲得MEC節(jié)點(diǎn)候選集$M{\rm{(}}t{\rm{)}}$； (5) If $\left( {M{\rm{(}}t{\rm{) = }}\varnothing } \right)||\left( {E_{\rm{L}}^*{\rm{(}}t{\rm{)}} < E_{{k_{{\rm{min}}}}}^*{\rm{(}}t{\rm{)}}} \right)$ (6) If ${f^*}{\rm{(}}t{\rm{) > }}{f_{{\rm{max}}}}$丟棄任務(wù)，$E{\rm{(}}t{\rm{) = }}{E_0}$； (7) Else 本地處理，$E{\rm{(}}t{\rm{) = }}E_{\rm{L}}^*{\rm{(}}t{\rm{)}}$； (8) End If (9) Else (10) 根據(jù)式(2)求得隱私量$q(t)$； (11) 根據(jù)策略2求得最優(yōu)解${\alpha ^*}{\rm{(}}t{\rm{)}}$； (12) 根據(jù)${\alpha ^*}{\rm{(}}t{\rm{)}}$執(zhí)行卸載并根據(jù)式(5)更新隱私量$Q{\rm{(}}t{\rm{)}}$； (13) End If (14) End For 
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表 2 參數(shù)設(shè)置
參數(shù) 取值 單位時(shí)隙長(zhǎng)度${l_s}$ 1 ms 信道增益$h_k^2$服從指數(shù)分布，均值$\overline {h_k^2} $ –90 dB 信道增益$h_k^2$服從指數(shù)分布，量化步長(zhǎng)${\delta _{h_k^2}}$ $\overline {h_k^2} /100$ 上行鏈路帶寬$W$ 1 MHz 噪聲功率密度${N_0}$ ${10^{ - 19}}\;{\rm{W/Hz}}$ CPU最大頻率${f_{\max}}$ 1.5 GHz 能耗系數(shù)$\kappa $ ${10^{ - 28}}$[16] 終端天線最大發(fā)射功率${p_{\max}}$ 1 W 任務(wù)大小b ${10^3}$ bit 處理1 bit數(shù)據(jù)所需CPU循環(huán)數(shù)$\beta $ 700 任務(wù)截止時(shí)間$\xi {\rm{(}}t{\rm{)}}$服從均勻分布 $\left\{ {0.1{l_s},0.2{l_s}, ··· ,{l_s}} \right\}$ 任務(wù)丟棄代價(jià)E0 $10 \cdot \kappa \beta bf_{{\rm{max}}}^2$
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