Neural Network-Enhanced Self-Organization for Efficient Resource Allocation in Distributed Edge Computing Environments

Containerization has transformed application deployment across diverse environments in the edge-cloud computing continuum, providing lightweight, portable solutions that integrate seamlessly across various cloud-native environments. However, default scheduling often leads to resource underutilization due to overestimated requests. This results in inaccurate demand prediction and static allocation strategies. This paper enhances a bottom-up, self-organizing approach with an NNbased peer selection mechanism, enabling intelligent resource allocation while maintaining decentralization. The proposed NNbased approach enables a data-driven selection strategy that significantly improves resource utilization and system performance; a 21% improvement in resource utilization is reported in a high-traffic scenario compared to the predecessor framework. The findings contribute to the broader research community by demonstrating the potential of combining agent-based modeling with machine learning techniques, paving the way for more adaptive and efficient edge orchestration systems.