TIIS (한국인터넷정보학회)
Current Result Document : 85 / 85
| 한글제목(Korean Title) |
Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning |
| 영문제목(English Title) |
Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning |
| 저자(Author) |
Si-yuan Sun
Ying Zheng
Jun-hua Zhou
Jiu-xing Weng
Yi-fei Wei
Xiao-jun Wang
|
| 원문수록처(Citation) |
VOL 15 NO. 07 PP. 2496 ~ 2512 (2021. 07) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption. |
| 키워드(Keyword) |
Deep Reinforcement Learning
green energy
heterogeneous service
Mobile Edge Computing
power consumption
Resource Allocation
|
| 파일첨부 |
PDF 다운로드
|
