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

TL;DR

This paper formulates the service migration problem in mobile edge computing as a Markov Decision Process, providing an optimal policy approximation that improves migration decisions amid user mobility.

Contribution

It introduces a novel MDP-based framework for optimal service migration, including a new algorithm for faster computation and practical evaluation with real-world mobility data.

Findings

MDP formulation effectively models migration decisions.

Proposed algorithm significantly reduces computation time.

Solution outperforms baseline methods in real-world scenarios.

Abstract

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 uniform one-dimensional user mobility while it provides a close approximation for uniform two-dimensional 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 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 superior performance of the proposed solution compared to baseline solutions.