邊緣計(jì)算中面向緩存的遷移決策和資源分配

楊守義; 韓昊錦; 郝萬(wàn)明; 陳怡航

doi:10.11999/JEIT240427

邊緣計(jì)算中面向緩存的遷移決策和資源分配

doi: 10.11999/JEIT240427 cstr: 32379.14.JEIT240427

鄭州大學(xué)電氣與信息工程學(xué)院鄭州 450001

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(U1604159)

詳細(xì)信息

作者簡(jiǎn)介:
楊守義：男，教授，研究方向?yàn)闊o(wú)線(xiàn)移動(dòng)通信，毫米波通信，移動(dòng)云計(jì)算等

韓昊錦：男，碩士生，研究方向?yàn)橐苿?dòng)邊緣計(jì)算、無(wú)線(xiàn)通信等

郝萬(wàn)明：男，副教授，研究方向?yàn)楹撩撞ㄍㄐ?，太赫茲通信，大?guī)模MIMO技術(shù)，物理層安全技術(shù)，智能超表面技術(shù)等

陳怡航：女，碩士生，研究方向?yàn)橐苿?dòng)邊緣計(jì)算

通訊作者:
楊守義　iesyyang@zzu.edu.cn

中圖分類(lèi)號(hào): TN92
計(jì)量
- 文章訪(fǎng)問(wèn)數(shù): 448
- HTML全文瀏覽量: 162
- PDF下載量: 66
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-05-29
- 修回日期: 2024-11-07
- 網(wǎng)絡(luò)出版日期: 2024-11-12
- 刊出日期: 2024-12-01

Cache Oriented Migration Decision and Resource Allocation in Edge Computing

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (U1604159)

摘要

摘要: 邊緣計(jì)算通過(guò)在網(wǎng)絡(luò)邊緣側(cè)為用戶(hù)提供計(jì)算資源和緩存服務(wù),可以有效降低執(zhí)行時(shí)延和能耗。由于用戶(hù)的移動(dòng)性和網(wǎng)絡(luò)的隨機(jī)性，緩存服務(wù)和用戶(hù)任務(wù)會(huì)頻繁地在邊緣服務(wù)器之間遷移，增加了系統(tǒng)成本。該文構(gòu)建了一種基于預(yù)緩存的遷移計(jì)算模型，研究了資源分配、服務(wù)緩存和遷移決策的聯(lián)合優(yōu)化問(wèn)題。針對(duì)這一混合整數(shù)非線(xiàn)性規(guī)劃問(wèn)題，通過(guò)分解原問(wèn)題，分別采用庫(kù)恩塔克條件和二分搜索法對(duì)資源分配進(jìn)行優(yōu)化，并提出一種基于貪婪策略的遷移決策和服務(wù)緩存聯(lián)合優(yōu)化算法(JMSGS)獲得最優(yōu)遷移決策和緩存決策。仿真結(jié)果驗(yàn)證了所提算法的有效性，實(shí)現(xiàn)系統(tǒng)能耗和時(shí)延加權(quán)和最小。
- 邊緣計(jì)算 /
- 遷移策略 /
- 服務(wù)緩存 /
- 資源分配
Abstract: Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
- Edge computing /
- Migrate strategy /
- Service cache /
- Resource allocation

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圖 1 系統(tǒng)模型

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圖 2 用戶(hù)偏好影響

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圖 3 帶寬對(duì)系統(tǒng)成本影響

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圖 4 應(yīng)用程序?qū)ο到y(tǒng)成本影響

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圖 5 服務(wù)器數(shù)量對(duì)系統(tǒng)成本影響

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圖 6 任務(wù)量大小對(duì)系統(tǒng)成本影響

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1 二分搜索的上行傳輸功率分配算法

初始化：傳輸功率$ {p_i} $范圍，收斂閾值$r$
(1) 根據(jù)式(21)計(jì)算得出$ \phi (p_i^{{\text{max}}}) $
(2) if $ \phi (p_i^{{\text{max}}}) \lt 0 $ then
(3) 　$ p_i^* = p_i^{{\text{max}}} $
(4) else
(5) 初始化參數(shù)$ {p_l} = p_i^{{\text{min}}} $, $ {p_h} = p_i^{{\text{max}}} $
(6) 　end if
(7) 　if $ \phi ({p_m}) < 0 $ then
(8) 　${p_l} = {p_m}$
(9) 　else
(10) $ {p_h} = {p_m} $
(11) end if
(12) until $({p_h} - {p_l}) \le r$
(13) $ p_i^* = ({p_l} + {p_h})/2 $

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2 基于貪婪決策的遷移緩存聯(lián)合優(yōu)化算法

初始化：${N_{{\text{local}}}}{\text{ = }}{N_{\text{0}}}$, ${N_{{\text{mec}}}} = \phi $
(1) for $i{\text{ = 1:}}N$
(2) 　for $m{\text{ = 1:}}M_i^{{\text{sort}}}$
(3) 　計(jì)算用戶(hù)的代價(jià)增益函數(shù)$\Delta C(m)$
(4) end for
(5) 將每個(gè)用戶(hù)的代價(jià)增益函數(shù)倒序排列，加入序列$N_i^{{\text{sort}}}$
(6) for $ i{\text{ = 1:}}N_i^{{\text{sort}}} $ 計(jì)算目標(biāo)函數(shù)值
(7) 　if ${\text{ET}}{{\text{C}}_{o + i}}{\text{ \lt ET}}{{\text{C}}_o}$
(8) 　　$ \alpha = 1 $, $ \vartheta = 1 $ or $ \varpi $=1
(9) 　else
(10) 保持原有模式
(11) end if
(12) if $ \varpi = 1 $, $ C_m^{\text} + C_m^{\text{a}} \le C_m^{\max } $
(13) 將應(yīng)用程序緩存至服務(wù)器
(14) else if $ X_m^{\min } < {X_i} $
(15) 　更新服務(wù)器狀態(tài)
(16) else
(17) 　本地執(zhí)行
(18) end if

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表 1 仿真參數(shù)

參數(shù)	數(shù)值
任務(wù)大小$\lambda $(Mb)	10～20
應(yīng)用程序大小$b$(Gb)	1～5
本地計(jì)算能力${f_{{\text{loc}}}}$(GHz)	0.5～1.5
邊緣服務(wù)器數(shù)目(個(gè))	5～20
噪聲功率譜密度${N_{\text{0}}}$(dBm/Hz)	–174
系統(tǒng)帶寬B(MHz)	1～2
服務(wù)器計(jì)算能力${f_{{\text{es}}}}$(GHz)	15～25
服務(wù)器緩存容量(Gb)	20～30

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