Abstract
By hosting cloud-based services at the network edge, mobile edge cloud has shown great advantage in reducing network overhead and latency of cloud services. In large scale mobile edge cloud, service migration is inevitable with the users movements, to guarantee service quality and reduce service cost. Due to the uncertainty of the users movements, it is very challenging to find the optimal migration strategy. In this paper, we study the problem of dynamic service migration in the mobile edge cloud for cost minimization. We formulate the problem as a Markov Decision Process (MDP) problem, which captures general cost models and provides a mathematical framework to design optimal service migration policies. However, solving the MDP problem suffers from the curse of dimensionality. To deal with this problem, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic service migration method, which reduces the dimension of state space from a multi-dimensional mobility pattern to a two-dimensional mobility pattern. The extensive simulation and numerical results show that the approximate MDP method significantly reduces the cost of migration and transmission.
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Acknowledgment
This research was supported by the NSF of China (Grant No. 61673354, 61602199, 61672474, 61402425, 61501412) and the Provincial NSF of Hubei (Grant No. 2016CFB107, 2015CFB400). This paper has been subjected to Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.
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Chen, Z., Yao, H., Gu, L., Zeng, D., Zheng, K. (2018). Dynamic Service Migration via Approximate Markov Decision Process in Mobile Edge-Clouds. In: Fortino, G., Ali, A., Pathan, M., Guerrieri, A., Di Fatta, G. (eds) Internet and Distributed Computing Systems. IDCS 2017. Lecture Notes in Computer Science(), vol 10794. Springer, Cham. https://doi.org/10.1007/978-3-319-97795-9_2
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DOI: https://doi.org/10.1007/978-3-319-97795-9_2
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