Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/171370
Title: Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Quanxin Zhao;Longjiang Li;Xin Peng;Li Pan;Sabita Maharjan;Yan Zhang
subject: 5G|Energy-efficiency|offloading|mobile edge computing
Year: 2016
Publisher: IEEE
Abstract: Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.
Description: 
URI: http://localhost/handle/Hannan/171370
ISSN: 2169-3536
volume: 4
More Information: 5896
5907
Appears in Collections:2016

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Title: Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Quanxin Zhao;Longjiang Li;Xin Peng;Li Pan;Sabita Maharjan;Yan Zhang
subject: 5G|Energy-efficiency|offloading|mobile edge computing
Year: 2016
Publisher: IEEE
Abstract: Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.
Description: 
URI: http://localhost/handle/Hannan/171370
ISSN: 2169-3536
volume: 4
More Information: 5896
5907
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7553459.pdf5.28 MBAdobe PDFThumbnail
Preview File
Title: Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Quanxin Zhao;Longjiang Li;Xin Peng;Li Pan;Sabita Maharjan;Yan Zhang
subject: 5G|Energy-efficiency|offloading|mobile edge computing
Year: 2016
Publisher: IEEE
Abstract: Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.
Description: 
URI: http://localhost/handle/Hannan/171370
ISSN: 2169-3536
volume: 4
More Information: 5896
5907
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7553459.pdf5.28 MBAdobe PDFThumbnail
Preview File