Decentralized AI Service Placement, Selection and Routing in Mobile Networks

TL;DR

This paper introduces a decentralized framework for AI service placement, selection, and routing in mobile networks, addressing the challenges of user mobility and latency to improve service quality.

Contribution

It proposes a joint optimization approach using a decentralized Frank--Wolfe algorithm with a novel messaging protocol to enhance AI service management in mobile environments.

Findings

Significant performance improvements over existing methods.

Effective handling of user mobility via traffic tunneling.

Optimization of service quality and latency trade-offs.

Abstract

The rapid development and usage of large-scale AI models by mobile users will dominate the traffic load in future communication networks. The advent of AI technology also facilitates a decentralized AI ecosystem where small organizations or even individuals can host AI services. In such scenarios, AI service (models) placement, selection, and request routing decisions are tightly coupled, posing a challenging yet fundamental trade-off between service quality and service latency, especially when considering user mobility. Existing solutions for related problems in mobile edge computing (MEC) and data-intensive networks fall short due to restrictive assumptions about network structure or user mobility. To bridge this gap, we propose a decentralized framework that jointly optimizes AI service placement, selection, and request routing. In the proposed framework, we use traffic tunneling to support user mobility without costly AI service migrations. To account for nonlinear queuing delays, we formulate a nonconvex problem to optimize the trade-off between service quality and end-to-end latency. We derive the node-level KKT conditions and develop a decentralized Frank--Wolfe algorithm with a novel messaging protocol. Numerical evaluations validate the proposed approach and show substantial performance improvements over existing methods.