FAST-RAM: A Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC

被引：0

作者：

Song, Tongyu ^{[1
]}

Hu, Wenyu ^{[1
]}

Tan, Xuebin ^{[1
]}

Ren, Jing ^{[1
]}

Wang, Sheng ^{[1
]}

Xu, Shizhong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China

来源：

2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | 2020年

基金：

国家重点研发计划;

关键词：

Mobile Edge Computing; Deep Neural Network; Task Offloading; Resource Allocation;

D O I：

10.1109/GLOBECOM42002.2020.9322645

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As one of the key concepts in the 5G network, MEC can support the latency-sensitive and compute-intensive services by widely deploying computing and storage capacity to the base stations at the network edge. Because these services are sensitive to latency, the joint optimization problem of task (offloading and resource allocation needs to be solved in a short time. In this paper, we propose a Fast Al-assistant Solution for Task Offloading and Resource Allocation in MEC (FAST-RAM), which can directly solve the joint optimization problem leveraging a deep neural network. FAST-RAM can produce the offloading policy and resource allocation scheme in milliseconds. Meantime, our solution has near-optimal performance and sufficient feasibility under different network environments.

引用

页数：6

共 47 条

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