Availability-Aware and Efficiency-Driven AI Service Chain Provisioning in Multi-Domain Edge Intelligence Cloud
For network operators managing multi-domain edge clouds, this work addresses the challenge of efficient and economical AI service chain provisioning under heterogeneity and limited visibility.
The paper tackles AI service chain provisioning in multi-domain edge intelligence clouds, proposing a multi-agent approach that reduces provisioning cost, delay, and improves availability. Experiments show the approach outperforms benchmarks across varying network topologies and numbers of local edge intelligence clouds.
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).