Fairness-Aware Dynamic Pricing and Service Routing for Cloud-Edge Systems via Heterogeneous Multi-Agent Learning
Cloud service platforms are increasingly extended to cloud-edge continua to support latency-sensitive and computation-intensive applications. In such distributed service environments, heterogeneous and time-varying compute capacity across edge sites creates strong competition among users, making it difficult to jointly achieve low delay and energy consumption, sustainable provider profit, and fair resource sharing. Although existing studies have investigated efficiency optimization, pricing mechanisms, and fairness-aware resource allocation, the joint coordination of adaptive pricing incentives and long-term fairness in dynamic multi-agent cloud-edge systems remains insufficiently explored. To address this issue, we develop a fairness-aware pricing and service-routing framework for multi-user multisite cloud-edge systems, and propose a heterogeneous multiagent learning method in which user agents learn service-routing decisions while service-node agents jointly adapt pricing and CPU-allocation policies under a fairness-aware utility design. The resulting coupled decision process is formulated as a Multi-Agent Markov Decision Process and implemented using a Multi-Agent Actor-Critic framework under centralized training and decentralized execution. Simulation results show that the proposed method reduces p95 delay and worst-user delay by up to 39.1% and 52.9%, respectively, while improving provider-side profit by up to 59.5% relative to the strongest competing baselines.