Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process (1506.05261v2)

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.

• The paper proposes an MDP framework with a distance-based approximation to simplify the state representation for service migration in MEC.
• It introduces a closed-form solution algorithm that reduces computational complexity from O(N³) to O(N²), significantly enhancing efficiency.
• Real-world evaluations using mobility traces demonstrate near-optimal performance with up to 99.9% faster computation, validating its practical applicability.

Dynamic Service Migration in Mobile Edge Computing Using Markov Decision Process

The paper "Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process" presents a comprehensive paper on optimizing service migration in Mobile Edge Computing (MEC) environments by formulating the problem as a Markov Decision Process (MDP). The goal is to strategically determine when and where to migrate cloud-based services hosted on local edge servers to minimize network overhead and latency, particularly as users change locations. This is crucial because the benefits of MEC, such as reduced service access delay, can only be maximally harnessed if services dynamically follow the user, reducing the distance for data transmission.

Key Contributions

1. MDP Formulation and Distance-Based Approximation: The authors provide an elaborate MDP framework to solve the service migration problem, capturing general cost models related to both migration and transmission. They introduce a distance-based approximation to manage the MDP's complexity, capturing the state by user-service distance instead of the user and service locations separately. This approximation is shown to be exact for uniform one-dimensional mobility and a close approximation for two-dimensional cases, offering a bounded constant error.
2. Efficient Solution Algorithm: To make the problem tractable, the authors develop an algorithm that relies on a closed-form solution to the MDP’s balance equation, significantly improving computational efficiency. This allows the solution to reduce complexity from $O(N^3)$ to $O(N^2)$ compared to standard value or policy iteration methods, where $N$ denotes the number of states.
3. Real-World Application and Evaluation: The paper includes evaluations using real-world mobility traces (e.g., San Francisco taxis) and highlights the superior performance of their proposed solution compared to baseline policies (e.g., never-migrate, always-migrate, and myopic strategies). The practical aspects consider limitations such as finite service capacities and non-universal edge server deployment.

Numerical Results and Insights

The authors present strong numerical results demonstrating that their distance-based approximation achieves near-optimal performance while maintaining high computational efficiency. For two-dimensional random walk mobility, the proposed method achieved computational time reductions of up to 99.9% compared to traditional methods while maintaining close to optimal costs. This illustrates the applicability and scalability of the solution in realistic urban MEC scenarios.

Implications and Future Work

The paper highlights significant implications for both theoretical and practical domains in MEC systems. The efficient migration policies derived from this work enable MEC systems to adapt promptly to user movements, crucial for applications requiring low latency, such as augmented reality and real-time video processing. On a theoretical level, their approach to simplifying MDP with the distance-based model may be applicable to other complex decision-making problems in dynamic environments.

Future work could explore extending the models to more complex mobility patterns or integrating machine learning techniques to predict user movements more precisely. Furthermore, the implementation of distributed service migration policies leveraging insights from this paper could enhance MEC's scalability across broader and more varied geographic regions.

In conclusion, this paper provides a robust framework for understanding and optimizing dynamic service migration in MEC, contributing both methodological advancements in MDP approximations and pragmatic solutions applicable to evolving cloud edge computing networks.