Load Balancing and Alternative Path Selection in Self-Organized Networks: A Data Plane Approach

This paper presents a novel load balancing algorithm tailored for deeply programmable networks, offering a decentralized approach to optimizing packet forwarding and load distribution. Unlike conventional systems reliant on centralized controllers or manual configurations, this algorithm operates entirely within the data plane, leveraging controlled flooding to dynamically discover and reroute traffic based on real-time congestion data. By detecting latency spikes indicative of congestion, switches autonomously select alternative paths to maintain optimal traffic flow. Implemented in the P4 data plane programming language, the algorithm is rigorously evaluated against existing load balancing methods for self-organized networks. The results demonstrate significant reductions in data transmission times, improved path symmetry, and enhanced scalability under various network conditions, making it a robust solution for modern, self-organized, and high-performance networks.