Joint Task Offloading and Service Caching for Multi-Access Edge Computing in WiFi-Cellular Heterogeneous Networks

被引:19
|
作者
Fan, Wenhao [1 ,2 ]
Han, Junting [1 ,2 ]
Su, Yi [1 ,2 ]
Liu, Xun [1 ,2 ]
Wu, Fan [1 ,2 ]
Tang, Bihua [1 ,2 ]
Liu, Yuan'an [1 ,2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Beijing Key Lab Work Safety Intelligent Monitorin, Beijing 100876, Peoples R China
基金
北京市自然科学基金;
关键词
Task analysis; Wireless fidelity; Servers; Cellular networks; Heterogeneous networks; Energy consumption; Delays; Edge computing; task offloading; service caching; channel allocation; resource management; RESOURCE-ALLOCATION; COMPUTATION;
D O I
10.1109/TWC.2022.3178541
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Enabled by Multi-access Edge Computing (MEC) in a WiFi-cellular heterogeneous network, the tasks of mobile terminals (MTs) can be offloaded via the cellular network to the MEC servers or cloud server, or via the WiFi network to alleviate transmission congestion of the cellular network. The MEC also enables service caching to cache the programs/libraries/databases of the tasks to avoid repeated input data uploading. Existing research works lack joint optimization on the task offloading and service caching for MEC in the WiFi-cellular heterogeneous network. In this paper, a novel resource management scheme for joint task offloading and service caching is proposed to maximize the energy consumption benefits of all the MTs covered by a WiFi-cellular heterogeneous network while guaranteeing the task processing delay tolerance of each MT. We consider the constraints on limited computing and storage resources of the MEC servers equipped on the cellular base station and the WiFi access point, and we also consider cellular channel allocation for the task offloading. We design an iterative algorithm based on the alternating optimization technique to solve the proposed mixed integer nonlinear programming problem efficiently. Extensive simulations are conducted in multiple scenarios by varying different crucial parameters. The numerical results demonstrate that our scheme can largely improve the system performance in all the scenarios, and energy consumption reduction optimized by our scheme is 16.24%-43.09% higher than those by the comparative works.
引用
收藏
页码:9653 / 9667
页数:15
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