Distributed learning-based context-aware SFC deployment in the Artificial Intelligence of Things

Abstract

With the rapid development of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies, the Artificial Intelligence of Things (AIoT) has become a key driving force for realizing intelligent and automated applications. The deployment of Service Function Chains (SFCs) is crucial in dynamic AIoT environments, where efficiently and flexibly deploying SFCs to meet real-time application demands is a research focus. However, existing SFC deployment methods often face challenges such as dynamic variations and uncertainty in contextual information, resource allocation inefficiencies, and limited adaptability to changing network conditions. To address these issues, we propose a learning-based context-aware dynamic SFC deployment method tailored for AIoT environments. Specifically, we introduce an attention-based contextual feature extraction method to capture dynamic changes (e.g., link latency variations) and prioritize key contextual information, improving the rate of served requests by 17.90% (69.60% vs. 59.03% for MADDPG) and enhancing the flexibility of SFC deployment decisions. Additionally, to address resource allocation bottlenecks and adaptability challenges in SFC deployment, we propose a distributed learning-based context-aware approach that uses collaborative learning and periodic updates (every 200 ms) to adjust SFC deployment strategies in response to topology changes and load variations and optimize system performance. Extensive experimental results demonstrate the efficacy of the proposed algorithm. Numerical results demonstrate that our algorithm reduces SFC deployment latency by 8% (46 ms vs. 50 ms for MADDPG), achieves 98.3% computational resource utilization, processes 211 Mbit/s service data volume, and improves adaptability to network changes, as validated in simulations.