Optimizing Edge Device Routing in Edge Computing: Harnessing the Synergy of Distributed Processing and Correlation Analysis
Hoa. Doan Nguyen Thanh, Phu Ngoc Thien Nguyen, Bang Cong Bui, Nghia. Phan Duc · 2024
Edge computing, characterized by its decentralized architecture, demands sophisticated techniques to optimize the allocation of computational tasks across diverse edge devices. This paper presents a potential approach for enhancing edge device routing through the synergistic integration of distributed processing and correlation analysis. The utilization of distributed processing enables decentralized task allocation, ensuring efficient resource utilization and load balancing. Concurrently, correlation analysis techniques are employed to uncover intricate relationships and dependencies among tasks and devices. By harnessing these correlations, the system makes intelligent, context-aware routing decisions, anticipating task requirements and predicting network conditions. This innovative integration empowers edge computing environments with adaptive load balancing, predictive optimization, and scalability. The proposed method significantly reduces the time required for selecting an edge device to process data packets, achieving nearly 50% reduction in comparison to two randomly selected methods. The proposed methodology not only ensures the efficient allocation of tasks but also lays the foundation for resilient and responsive edge computing ecosystems, transforming the landscape of edge-enabled applications and services.