Journal
2019 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2019)
Volume -, Issue -, Pages 91-98Publisher
IEEE
DOI: 10.1109/ICWS.2019.00026
Keywords
Edge User Allocation; Mobile Service Computing; Mobile Edge Computing; Mobility; Quality-of-Service
Funding
- Royal Society of the UK [61611130209]
- National Natural Science Foundation of China [61611130209]
- National Science Foundalions of China [61472051/61702060]
- Fundamental Research Funds for the Central Universities [2019CDXYJSJ0022]
- Science Foundation of Chongqing [cstc2017jcyjA1276]
- China Postdoctoral Science Foundation [2015m570770]
- Chongqing Postdoctoral Science special Foundation [Xm2015078]
- Universities' Sci-tech Achievements Transformation Project of Chongqing [KJZH17104]
- Chongqing grand RD projects [cstc2017zdcy-zdyf0120, cstc2017rgznzdyf0118]
- Natural Science Foundation of Chongqing [cstc2016jcyjA1315]
- National Key R&D Program of China [2018YFD1100304]
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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.
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