Mobility-Aware Content Caching and User Association for Ultra-Dense Mobile Edge Computing Networks

被引:22
作者
Li, Hui [1 ]
Sun, Chuan [1 ]
Li, Xiuhua [1 ]
Xiong, Qingyu [1 ]
Wen, Junhao [1 ]
Wang, Xiaofei [2 ]
Leung, Victor C. M. [3 ,4 ]
机构
[1] Chongqing Univ, Sch Big Data & Software Engn, Chongqing, Peoples R China
[2] Tianjin Univ, Coll Intelligence & Comp, TKLAN, Tianjin, Peoples R China
[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
[4] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC, Canada
来源
2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | 2020年
基金
国家重点研发计划; 加拿大自然科学与工程研究理事会;
关键词
D O I
10.1109/GLOBECOM42002.2020.9348257
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the tremendous growth of mobile data traffic generated by various devices such as smartphones, smartpads and wearable devices, it is necessary for mobile network operators to introduce revolutionary networking techniques, thereby satisfying service requirements of mobile users. Recently, mobile edge computing (MEC) has been regarded as an effective technique to alleviate the traffic burden on backhaul networks. In this paper, we investigate the issue of mobility-aware content caching and user association for ultra-dense MEC networks by minimizing the system costs. The problem is formulated as a complex pure integer nonlinear programming, which is NP-hard. To address the original long-term optimization problem, we decompose it into a series of one-slot subproblems, and then optimize the short-term subproblem in two phases (i.e., content caching and user association). We further propose a mobility-aware online caching algorithm to achieve content caching, and a lazy re-association algorithm to determine user association based on matching theory. Trace-driven evaluation results demonstrate that the proposed framework has superior performance on reducing system costs.
引用
收藏
页数:6
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