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Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach

2021

In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …

Computer scienceReal-time computingTP1-118502 engineering and technologyBiochemistryVDP::Teknologi: 500::Elektrotekniske fag: 540ArticleAnalytical Chemistry0202 electrical engineering electronic engineering information engineeringcomputational offloadingElectrical and Electronic EngineeringInstrumentationenergy efficiencyMobile edge computingArtificial neural networkbusiness.industryChemical technologyDeep learningdeep learning020206 networking & telecommunicationsEnergy consumptionAtomic and Molecular Physics and OpticsTask (computing)cost functionUser equipment020201 artificial intelligence & image processingmobile edge computingArtificial intelligenceEnhanced Data Rates for GSM Evolutionremote executionbusinessEfficient energy useSensors
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