A Comparative Study on Service Migration for Mobile Edge Computing Based on Deep Learning

Abstract

Over the past few years, Deep Learning (DL), a promising technology leading the next generation of intelligent environments, has attracted significant attention and has been intensively utilized in various fields in the fourth industrial revolution era. The applications of Deep Learning in the area of Mobile Edge Computing (MEC) have achieved remarkable outcomes. Among several functionalities of MEC, the service migration frameworks have been proposed to overcome the shortcomings of the traditional methodologies in supporting high-mobility users with real-time responses.

The service migration in MEC is a complex optimization problem that considers several dynamic environmental factors to make an optimal decision on whether, when, and where to migrate. In line with the trend, various service migration frameworks based on a variety of optimization algorithms have been proposed to overcome the limitations of the traditional methodologies. However, it is required to devise a more sophisticated and realistic model by solving the computational complexity and improving the inefficiency of existing frameworks. Therefore, an efficient service migration mechanism that is able to capture the environmental variables comprehensively is required.

In this thesis, we propose an enhanced service migration model to address user proximity issues. We first introduce innovative service migration models for single-user and multi-user to overcome the users’ proximity issue while enforcing the service execution efficiency. Secondly, We formulate the service migration process as a complicated optimization problem and utilize Deep Reinforcement Learning (DRL) to estimate the optimal policy to minimize the migration cost, transaction cost, and consumed energy jointly. Lastly, we compare the proposed models with existing migration methodologies through analytical simulations from various aspects. The numerical results demonstrate that the proposed models can estimate the optimal policy despite the computational complexity caused by the dynamic environment and high-mobility users.