Mobility-Aware Cooperative Service Caching for Mobile Augmented Reality Services in Mobile Edge Computing

被引:0
|
作者
Fan, Qingyang [1 ]
Zhang, Weizhe [2 ,3 ]
Ling, Chen [2 ]
Yadav, Rahul [4 ]
Wang, Desheng [5 ]
He, Hui [2 ]
机构
[1] Harbin Inst Technol, Sch Cyberspace Sci, Harbin 150001, Peoples R China
[2] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
[3] Peng Cheng Lab, Dept New Networks, Shenzhen 518000, Peoples R China
[4] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China
[5] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China
基金
国家重点研发计划;
关键词
Servers; Delays; Task analysis; Resource management; Costs; Augmented reality; Genetic algorithms; Edge computing; service caching; mobile augmented reality; genetic algorithm;
D O I
10.1109/TVT.2024.3422179
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
Mobile edge computing (MEC) plays a significant role in reducing network delay for Mobile Augmented Reality (MAR) services by caching these services close to the User Equipments (UEs). These MAR services collect UEs' network traffic and orientation information, and generate the service results back to UEs. However, the UE's mobility features change network traffic and orientation, negatively impacting MAR services' access frequencies and service preferences. Moreover, the changed access frequencies also influence the workload of cached MAR services, resulting in the uneven workload of edge servers. Therefore, this paper formalizes cooperative service caching based on UEs' location and orientation to optimize network delay and response fairness in MEC environments. To solve the problem, we propose a Service Caching strategy based on Regional Mobility features Awareness (SCRMA) algorithm, which consists of two stages. Firstly, the Regional Mobility features Awareness (RMA) algorithm perceives the user mobility features and service preferences, which provides a prerequisite for determining service caching strategy. Then, a Service Caching strategy based on a Genetic Algorithm (SCGA) is proposed to optimize network delay and response fairness. The simulation experiment on a real dataset shows that our service caching strategy averagely reduces network delay, fairness factor, and total cost by 11.49%, 33.24%, and 17.86% compared with the existing algorithms, respectively.
引用
收藏
页码:17543 / 17557
页数:15
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