MAESP: Mobility aware edge service placement in mobile edge networks

被引:15
|
作者
Zhao, Xuhui [1 ,2 ]
Shi, Yan [1 ]
Chen, Shanzhi [3 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China
[2] Henan Univ Sci & Technol, Luoyang, Peoples R China
[3] China Acad Telecommun Technol, State Key Lab Wireless Mobile Commun, Beijing, Peoples R China
基金
美国国家科学基金会;
关键词
Mobile edge networks; User mobility; Service placement; Multi-objective context multi-armed bandit; POLICY;
D O I
10.1016/j.comnet.2020.107435
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile edge networks can provide ultra-low latency by deploying services at the edge of networks. However, it is impracticable to place services on all the edge servers due to limited deployment cost requirement. It is desirable to make the optimal edge application placement decisions in a minimum response time and deployment cost, which involve two possibly conflicting objectives. An important issue here is that the number of edge application request from mobile users, which is the key factor determining the realization of two objectives, can vary considerably and the number of edge application request usually unknown before deploying edge application in a particular edge site. It has been found recently that human mobility (location and time features) have a strong influence on what kinds of application that mobile users choose to use. Based on the above observation, we investigate the mobility aware edge service placement problem aiming at optimizing service latency and deployment cost. The problem is formulated as a multi-objective optimization problem and can be solved by multi-objective context multi-armed bandit with a dominant objective. The features of user mobility (time, location) are considered as the context information guiding the edge application placement decisions. We develop mobility-aware edge service placement (MAESP) method and analyse performance measures of MAESP using the 2-dimensional (2D) regret. We show that the 2D regret of MAESP are sublinear in the number of rounds. Based on a real-world dataset, we carry out extensive simulations to evaluate the performance of MAESP. The results show that MAESP outperforms the benchmark algorithms.
引用
收藏
页数:13
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