RRO: A Regularized Routing Optimization Algorithm for Enhanced Throughput and Low Latency with Efficient Complexity
<|reference_start|>RRO: A Regularized Routing Optimization Algorithm for Enhanced Throughput and Low Latency with Efficient Complexity: In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in lightly-loaded networks, they often falter in the face of increasing congestion. Recent approaches have suggested utilizing backpressure and deep learning techniques for route optimization. However, these approaches face challenges due to their high implementation and computational complexity, surpassing the capabilities of networks with limited hardware devices.   A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this collaborative research between Ben-Gurion University and Ceragon Networks Ltd., we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond technologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.<|reference_end|>
@article{zenati2024rro:,
    title={RRO: A Regularized Routing Optimization Algorithm for Enhanced
  Throughput and Low Latency with Efficient Complexity},
    author={David Zenati, Tzalik Maimon, Kobi Cohen},
    journal={arXiv preprint arXiv:2407.18683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18683},
    primaryClass={cs.NI}
}