TY - GEN
T1 - Mobility-aware and migration-enabled online edge user allocation in mobile edge computing
AU - Peng, Qinglan
AU - Xia, Yunni
AU - Feng, Zeng
AU - Lee, Jia
AU - Wu, Chunrong
AU - Luo, Xin
AU - Zheng, Wanbo
AU - Liu, Hui
AU - Qin, Yidan
AU - Chen, Peng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.
AB - The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.
KW - Edge User Allocation
KW - Mobile Edge Computing
KW - Mobile Service Computing
KW - Mobility
KW - Quality-of-Service
UR - http://www.scopus.com/inward/record.url?scp=85072781431&partnerID=8YFLogxK
U2 - 10.1109/ICWS.2019.00026
DO - 10.1109/ICWS.2019.00026
M3 - Conference contribution
AN - SCOPUS:85072781431
T3 - Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services
SP - 91
EP - 98
BT - Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services
A2 - Bertino, Elisa
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Damiani, Ernesto
A2 - Damiani, Ernesto
A2 - Goul, Michael
A2 - Oyama, Katsunori
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Web Services, ICWS 2019
Y2 - 8 July 2019 through 13 July 2019
ER -