Reliability-Optimal Offloading in Low-Latency Edge Computing Networks: Analytical and Reinforcement Learning Based Designs

In this paper, we consider a multi-access edge computing (MEC) network with multiple servers. Due to the low latency constraints, the wireless data transmission/offloading is carried by finite blocklength codes. We characterize the reliability of the transmission phase in the finite blocklength regime and investigate the extreme event of queue length violation in the computation phase by applying extreme value theory. Under the assumption of perfect channel state information (CSI), we follow the obtained characterizations and provide an optimal framework design including server selection and time allocation aiming to minimize the overall error probability. Moreover, when only the outdated CSI is available, a deep reinforcement learning based design is proposed applying the deep deterministic policy gradient method. Via simulations, we validate the convexity proven in our analytical model and show the performance advantage of proposed analytical solution and learning-based solution comparing to the benchmark for perfect CSI and outdated CSI, respectively.

Automated summary

This paper investigates a multi-access edge computing network with multiple servers using finite blocklength codes for wireless data transmission/offloading. We analyze the reliability of the transmission phase in the finite blocklength regime and study extreme events of queue length violation in the computation phase using extreme value theory. By optimizing server selection and time allocation to minimize overall error probability, and proposing a deep reinforcement learning based design for cases with outdated CSI, we demonstrate performance advantages over benchmark solutions for perfect and outdated CSI through simulations.