QoE-Driven Multi-Task Offloading for Semantic-Aware Edge Computing Systems

TL;DR

This paper introduces a semantic-aware multi-modal task offloading framework for MEC systems, optimizing QoE by balancing latency, energy, and task performance using reinforcement learning.

Contribution

It proposes a novel semantic extraction factor and a unified QoE metric, formulating the offloading as an MDP and applying MAPPO for joint resource optimization.

Findings

Reduces execution latency by 18.1%

Lowers energy consumption by 12.9%

Extends to models with different user preferences

Abstract

Mobile edge computing (MEC) provides low-latency offloading solutions for computationally intensive tasks, effectively improving the computing efficiency and battery life of mobile devices. However, for data-intensive tasks or scenarios with limited uplink bandwidth, network congestion might occur due to massive simultaneous offloading nodes, increasing transmission latency and affecting task performance. In this paper, we propose a semantic-aware multi-modal task offloading framework to address the challenges posed by limited uplink bandwidth. By introducing a semantic extraction factor, we balance the relationship among transmission latency, computation energy consumption, and task performance. To measure the offloading performance of multi-modal tasks, we design a unified and fair quality of experience (QoE) metric that includes execution latency, energy consumption, and task performance. Lastly, we formulate the optimization problem as a Markov decision process (MDP) and exploit the multi-agent proximal policy optimization (MAPPO) reinforcement learning algorithm to jointly optimize the semantic extraction factor, communication resources, and computing resources to maximize overall QoE. Experimental results show that the proposed method achieves a reduction in execution latency and energy consumption of 18.1% and 12.9%, respectively compared with the semantic-unaware approach. Moreover, the proposed approach can be easily extended to models with different user preferences.