Energy-Efficient Caching for Mobile Edge Computing in 5G Networks
Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/7/6/557 |
| _version_ | 1811293973389508608 |
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| author | Zhaohui Luo Minghui LiWang Zhijian Lin Lianfen Huang Xiaojiang Du Mohsen Guizani |
| author_facet | Zhaohui Luo Minghui LiWang Zhijian Lin Lianfen Huang Xiaojiang Du Mohsen Guizani |
| author_sort | Zhaohui Luo |
| collection | DOAJ |
| description | Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS’s energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low. |
| first_indexed | 2024-04-13T05:09:36Z |
| format | Article |
| id | doaj.art-e6e9feedc5b149caaf5a940c89e3708d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T05:09:36Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6e9feedc5b149caaf5a940c89e3708d2022-12-22T03:01:04ZengMDPI AGApplied Sciences2076-34172017-05-017655710.3390/app7060557app7060557Energy-Efficient Caching for Mobile Edge Computing in 5G NetworksZhaohui Luo0Minghui LiWang1Zhijian Lin2Lianfen Huang3Xiaojiang Du4Mohsen Guizani5Department of Communications Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Communications Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Communications Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Communications Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USADepartment of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USAMobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS’s energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.http://www.mdpi.com/2076-3417/7/6/557edge cachingenergy-efficientmobile edge computing5G cellular networks |
| spellingShingle | Zhaohui Luo Minghui LiWang Zhijian Lin Lianfen Huang Xiaojiang Du Mohsen Guizani Energy-Efficient Caching for Mobile Edge Computing in 5G Networks Applied Sciences edge caching energy-efficient mobile edge computing 5G cellular networks |
| title | Energy-Efficient Caching for Mobile Edge Computing in 5G Networks |
| title_full | Energy-Efficient Caching for Mobile Edge Computing in 5G Networks |
| title_fullStr | Energy-Efficient Caching for Mobile Edge Computing in 5G Networks |
| title_full_unstemmed | Energy-Efficient Caching for Mobile Edge Computing in 5G Networks |
| title_short | Energy-Efficient Caching for Mobile Edge Computing in 5G Networks |
| title_sort | energy efficient caching for mobile edge computing in 5g networks |
| topic | edge caching energy-efficient mobile edge computing 5G cellular networks |
| url | http://www.mdpi.com/2076-3417/7/6/557 |
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