Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks

TL;DR

This paper introduces a novel framework using correlated variational autoencoders to model the distribution of end-to-end delay in MEC networks, enabling optimized reliability through risk-aware task allocation.

Contribution

It proposes a new risk-based optimization approach leveraging VAEs to improve MEC network reliability under delay uncertainties.

Findings

Outperforms baseline methods in reliability metrics.

Effectively models delay distribution using VAEs.

Enhances task allocation efficiency considering delay statistics.

Abstract

Multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC mandates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users' processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.