邊緣計算中動態(tài)服務(wù)器部署與任務(wù)卸載聯(lián)合優(yōu)化算法

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中圖分類號：TP393 文獻標志碼：A 文章編號：1001-3695（2025）06-031-1830-08

doi： 10.19734/j. issn.1001-3695.2024.11.0462

Joint optimization algorithm for dynamic server deployment and task offloading in edge computing

BaiWenchao，Lu Xianling? （SchoolofInternetofThings，JiangnanUniversity，WuxiJiangsu214122，China）

Abstract：Inmobileedgecomputing，thefixedlocations of edgeserverdeploymentscanleadtoimbalancedresourceutilizationof edgeservers，resultinginincreasedlatencyandenergyconsumptionduringthetask ofloading process.Toaddressthis issue，thispaperproposedaierarchicalreiforcementlearing-basedjointoptimizationalgorithm.Firstly，itdecomposedthe problemofedge serverplacementand task ofloading and transformedthemintoabi-Markovdecision processThen，itconstructedaglobalintellgentagentmodelforhigher-leveledgeserverdeploymentusingthedeepQ-network，andaccelerated modelconvergencebyintroducingthe K-means algorithmtoprovide high-qualitysamplesforthehigher-layer policy.Itbuilta lower-layer multi-agentmodelfortaskofloadingusingthemulti-agentproximalpolicyoptimizationalgorithm，andimproved trainingstabilitybyintroducingstatenormalizationtoreducethestatesfeaturescalediferencesinthelower-layerpolicy.Finally，itachievedtheultimateoptimizationgoaltroughalternatingoptimizationofthehigher-layerandlower-layerpolicies. Simulationresults indicatethattheproposedalgorithmcanachieveoptimal serverdeploymentandtask ofloading strategies， comparedtorandomstrategiesandotherreinforcementlearningalgorithm，and itdemonstratesgreater benefitsintermsof model training efficiency，target rewards，and load balancing metrics.

Key words：edge computing；task ofloading；edge server deployment；hierarchical reinforcement learning

0 引言

隨著5G和6G技術(shù)的高速發(fā)展，越來越多的計算密集型應(yīng)用出現(xiàn)[1]，如虛擬現(xiàn)實和面部識別等。（剩余20850字）