Overload protection in mobile edge computing using multi-agent reinforcement learning

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In recent years, there has been a proliferation of computationally intensive smartphone applications such as real-time online gaming, video-on-demand, augmented and virtual reality applications, etc. Due to the high computational demand, these services are often moved to what are called Edge Servers using a paradigm known as Mobile Edge Computing (MEC).To ensure the reliability of a MEC cluster, it is important to prevent overloading of the servers. Such server overloading can be prevented by pre-emptively offloading the requests and routing them to other servers or completely dropping them. In this project, we propose to develop new theories and algorithms to improve job offloading and routing policies using multi-agent reinforcement learning (MARL).Specifically, we will: (i) develop a multi-agent multi-stage optimization problem based mathematical model for multi-server MEC clusters; (ii) identify pertinent qualitative properties of optimal admission control and routing policies; (iii) develop low-complexity MARL algorithms which exploit the qualitative properties derived in the previous step; and (iv) perform a detailed simulation-based study to evaluate the performance of the proposed algorithms in real-world settings. It is anticipated that the project will lead to future publications in top-tier conferences and journals.