AI-Enhanced Load Balancing in Federated Edge Computing Networks: Challenges and Solutions

In federated edge computing networks, edge computing (EC) nodes from multiple service providers (SPs) are installed to serve the same customer base and each SP intends to maximize their economic utilities. Therefore, to address research problems like EC node placement, job request allocation, and load balancing, we need to involve some economic aspects along with network engineering aspects. This implies that we need to propose novel system models and formulate problems in a significantly different way than the existing ones. Thus, we are motivated to discuss about the primary aspects of these new problem formulations and propose a novel economic and game-theoretic model for load balancing among federated EC nodes in this paper. In this game formulation, instead of focusing on latency minimization, under-loaded EC nodes intend to maximize their economic utilities by receiving any extra workload and incentives from their overloaded neighbors while satisfying the expected latency target. Furthermore, we design a centralized control mechanism, tailor-made for ultra-reliable and low latency (uRLL) applications, for implementing this load balancing framework by incorporating artificial intelligence (AI)-enhanced traffic prediction algorithms.