Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process

Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer, Ting He, Kevin Chan, Kin K. Leung

Research output: Contribution to journalArticlepeer-review

189 Scopus citations


In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov decision process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for the uniform 1-D user mobility, while it provides a close approximation for uniform 2-D mobility with a constant additive error. We also propose a new algorithm and a numerical technique for computing the optimal solution, which is significantly faster than traditional methods based on the standard value or policy iteration. We illustrate the application of our solution in practical scenarios where many theoretical assumptions are relaxed. Our evaluations based on real-world mobility traces of San Francisco taxis show the superior performance of the proposed solution compared to baseline solutions.

Original languageEnglish (US)
Article number8727722
Pages (from-to)1272-1288
Number of pages17
JournalIEEE/ACM Transactions on Networking
Issue number3
StatePublished - Jun 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process'. Together they form a unique fingerprint.

Cite this