Enhancing AIGC Service Efficiency with Adaptive Multi-Edge Collaboration in A Distributed System

TL;DR

This paper introduces AMCoEdge, an adaptive multi-edge collaboration approach utilizing deep reinforcement learning to improve AIGC service efficiency by reducing delays and failure rates in distributed mobile edge computing environments.

Contribution

The paper proposes a novel adaptive multi-server MEC framework with an online RL-based algorithm, enhancing resource utilization and significantly reducing response times for AIGC services.

Findings

Achieves at least 11.04% reduction in task offloading make-span.

Decreases failure rate by 44.86%.

Reduces service delays by 9.23% to 31.98% in prototype tests.

Abstract

The Artificial Intelligence Generated Content (AIGC) technique has gained significant traction for producing diverse content. However, existing AIGC services typically operate within a centralized framework, resulting in high response times. To address this issue, we integrate collaborative Mobile Edge Computing (MEC) technology to reduce processing delays for AIGC services. Current collaborative MEC methods primarily support single-server offloading or facilitate interactions among fixed Edge Servers (ESs), limiting flexibility and resource utilization across all ESs to meet the varying computing and networking requirements of AIGC services. We propose AMCoEdge, an adaptive multi-server collaborative MEC approach to enhancing AIGC service efficiency. The AMCoEdge fully utilizes the computing and networking resources across all ESs through adaptive multi-ES selection and dynamic workload allocation, thereby minimizing the offloading make-span of AIGC services. Our design features an online distributed algorithm based on deep reinforcement learning, accompanied by theoretical analyses that confirm an approximate linear time complexity. Simulation results show that our method outperforms state-of-the-art baselines, achieving at least an 11.04% reduction in task offloading make-span and a 44.86% decrease in failure rate. Additionally, we develop a distributed prototype system to implement and evaluate our AMCoEdge method for real AIGC service execution, demonstrating service delays that are 9.23% - 31.98% lower than the three representative methods.