0000000000538273

AUTHOR

Sadia Khaf

showing 2 related works from this author

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

2020

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realis…

FOS: Computer and information sciencesComputer sciencemedia_common.quotation_subjectG.3Cloud computingComputer Science - Networking and Internet ArchitectureC.2.3BiomaterialsC.2.1Resource (project management)Electrical and Electronic EngineeringFunction (engineering)media_commonNetworking and Internet Architecture (cs.NI)Mobile edge computingbusiness.industryEnergy consumptionComputer Science ApplicationsTask (computing)User equipmentMechanics of MaterialsModeling and SimulationResource allocationG.3; C.2.3; C.2.1business46FxxComputer networkComputers, Materials & Continua
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A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

2019

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…

QA75General Computer ScienceComputer scienceDistributed computingenergy efficient offloading02 engineering and technologyVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 42001 natural sciencesuser equipmentComputational offloadingServer0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Mobile edge computingbusiness.industryDeep learning010401 analytical chemistryGeneral Engineeringdeep learning020206 networking & telecommunicationsEnergy consumption0104 chemical sciencesUser equipmentArtificial intelligencemobile edge computinglcsh:Electrical engineering. Electronics. Nuclear engineeringbusinesslcsh:TK1-9971Efficient energy useIEEE Access
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