Availability-Aware and Efficiency-Driven AI Service Chain Provisioning in Multi-Domain Edge Intelligence Cloud

2026-06-03

AI Summary

This study addresses the challenge of efficiently and reliably deploying AI service chains in multi-domain edge intelligent clouds, where heterogeneous environments, resource constraints, and partial observability of system states hinder performance. To tackle this problem, the authors formulate it as a partially observable stochastic game and propose a graph-temporal dual network (GTDN)-based multi-agent collaborative optimization method. This approach uniquely integrates network topology and temporal dependencies among services into a unified model, enabling joint optimization of cost, latency, and availability. Experimental results demonstrate that the proposed method significantly outperforms existing baselines across diverse network topologies and edge node scales, achieving substantial improvements in overall performance.

Abstract

In a multi-domain edge intelligence cloud (MDEIC) managed by multiple network operators, AI services are delivered by chains of virtual network functions (VNFs) executed in sequence, called AI service chains (AISCs). Therefore, achieving an efficient and economical AISC provisioning approach is essential. However, the interaction between the environmental characteristics (heterogeneity, resource constraints and limited information visibility) of MDEIC and the time-dependence of AISCs, introduces various challenges to AISC provisioning in MDEIC. In this paper, we first formulate the AISC provisioning problem as a partially observable stochastic game (POSG). Then, we propose a graph-and-time-based multi-agent AISC provisioning (GT-MAAISCP) approach to achieve the collaborative optimization of AISC provisioning cost, delay and availability. Specifically, each agent uses the graph-time dueling network (GTDN) architecture to extract network topology information and temporal relationships. Finally, the experimental results demonstrate that the proposed approach outperforms benchmark approaches in MDEIC and also illustrate its performance under varying network topologies and different numbers of local EICs (LEICs).

Problem

• AI service chain
• multi-domain edge intelligence cloud
• availability-aware provisioning
• resource orchestration
• time-dependent services

Innovation

• partially observable stochastic game
• graph-time dueling network
• multi-agent reinforcement learning
• AI service chain provisioning
• edge intelligence cloud