Improving Network Latency in RLNC-Enabled Cloud-Native 5G and Beyond: A Comparative Evaluation in Handling Data Traffic

To meet the stringent requirements of emerging 5G use cases that demand high reliability and low latency, the potential of in-network computing is realized by deploying Random Linear Network Coding (RLNC) recoders directly within the network infrastructure. However, the choice of network infrastructure configuration can significantly impact traffic latency as demonstrated in this paper. To investigate this, we compare the performance of RLNC recoder when implemented on two different switches, the Cisco Catalyst 9500 (C9500) and the Cisco Catalyst 9300 (C9300), focusing on reducing one-way delay (OWD). Our results demonstrate that the C9300 significantly outperforms the C9500, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5. 6 \%}$</tex> of haptic packets recovered within 70 ms compared to 85.8% for the C9500. Notably, the C9300 consistently minimizes packet loss better, especially in haptic traffic, where the C9300 with RLNC recoder reduces packet loss to nearly zero. Additionally, under stress conditions with a 10 Mbps transmission rate, the C9300 continues to excel in reducing OWD, although the in-network computing performance in both switches exhibits similar reliability challenges. This study focuses on analysing the performance of RLNC recoding in cloudnative <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 G}$</tex> systems under various hardware conditions.