基于双池DDPG的边缘计算卸载延时性能研究

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中图分类号：TN929.5-34 文献标识码：A 文章编号：1004-373X（2025）17-0117-07

DOI：10.16652/j.issn.1004-373x.2025.17.018引用格式：，等.基于双池DDPG的边缘计算卸载延时性能研究[J].现代电子技术，2025，48（17）：117-123.

Abstract：Withtheproliferationofcomputation-intensiveandlatency-sensitiveservices，itisdificultforthetraditional cloudcomputing modeltomettherequirementsoflowlatencyandhigh-qualityservices.Taskscanbeofloaded todgesubnets toreducelatencyandaleviatenetworkcongestionbythedistributededgecomputing mode.Inviewof theoffoadingproblemof collaborativecomputingonedgecomputingservers，acollaborativecomputing resourcealocationalgorithmbasedondep reinforcementlearningisproposed.Firstly，theoptimizationobjectiveisdeterminedaccordingtotheestablishedheterogneous edge network odel，thetasklatencyisinimied，andtheewardfunctionisiven.SecondlyaccordingtothetraditioalG （deepdeterministicpolicygradient）algorithm，thedual-polisintroducedtoclassifyandstorethedatatoimprovethequalityof empiricaldata.Finally，theagentistrainedtofindoutanoptimalstrategytoaapttotheenvironmenttoachieveboththeload balancingandtheresourceefectivealocationamongservers.Simulationresultsshowthat theimprovedD3PG（dual-poolDDPG） algorithmfullyconsiderstheheterogeneityofedgesubnetsandthemobilityofedgedevices，nditstask latencyisreducedby 3.8%～24.8%in comparison with that of the traditional learningalgorithms.

Keywords：heterogeneousnetwork；edge computing;computationofloading；task latency；deepreinforcementlearning; D3PG

0 引言

随着无线通信技术的发展，计算密集型和延迟敏感型边缘应用业务不断涌现，这对网络边缘侧的数据、算力、存储等需求大幅提高。（剩余11306字）