Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach

TL;DR

This paper presents a Lyapunov-guided reinforcement learning approach to optimize energy consumption for multi-task DNN inference in MEC-assisted XR devices, balancing resource allocation and queue stability.

Contribution

It introduces a novel distributed queue model and a dual time-scale optimization strategy with a Lyapunov-guided PPO algorithm for energy-efficient XR device operation.

Findings

Achieves 24.79% to 46.14% energy savings over baselines.

Reduces XR device energy consumption by 24.29% to 56.62%.

Outperforms existing algorithms in energy efficiency.

Abstract

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided Proximal Policy Optimization algorithm, named LyaPPO. Numerical results demonstrate that the LyaPPO algorithm outperforms the baselines, achieving energy conservation of 24.79% to 46.14% under varying resource capacities. Specifically, the proposed algorithm reduces the energy consumption of XR devices by 24.29% to 56.62% compared to baseline algorithms.