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$\mathbf{9 5. 6 \%}$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$\mathbf{5 G}$systems under various hardware conditions.

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN packet error handling using retransmission reduces packet loss but can increase transmission delay. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Previous research typically only simulates the integration of RLNC in 5G but does not demonstrate nor evaluate this integration in real 5G systems. In contrast, we implement and evaluate our approach through measurements with commercially available servers and switches, running the OpenAirInterface (OAI) 5G software stack. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols. Specifically, FlexNC provides a forwarder that flexibly interfaces with multiple RLNC protocols in a cloud-native (containerized) solution. Network operators can configure FlexNC based on network conditions and application requirements. For boosting network programmability, our Recoder in the Network (RecNet) leverages In-Network Computing (INC) in intermediate network nodes. We have developed an open-source cloud-native (Docker-based) implementation of both FlexNC and RecNet on OAI, including INC for the Recoder on a Cisco Catalyst switch. Measurements for video, haptic, and audio traffic indicate that i.) FlexNC adapts to various application needs in terms of latency and packet loss, and RecNet significantly reduces packet loss for a remote user with minimal increase in delay compared to pure RLNC. To the best of our knowledge, this is the first article to report testbed measurement results for cloud-native network coding in a 5G system, thus creating a baseline for reliable 5G communication.

Edge clouds embrace Network Function Virtualization (NFV) to bring network functions (NFs) closer to end devices. The mobility nature of edge clouds and the emergence of new use cases, such as robot control or metaverse, demand NFs with high resilience. However, numerous NFs like firewalls require stateful processing that depends on historical processing values, referred to as NF states. Consequently, supporting stateful NFs with high resilience necessitates the maintenance of their states, often resulting in significant modification of the NFs. While providing high resilience for NFs, maintaining their processing performance is a critical challenge for existing solutions. Our proposed framework, StateProc, eliminates this concern by enhancing the processing capabilities of NFs through the utilization of NF states for custom processing. Through custom processing for state transfer, the evaluation results show that StateProc significantly improves the state transfer time, up to 93% faster than a notable state-of-the-art framework, without additional processing overhead.

Emerging use cases, such as Tactile Internet, typically demand high reliability and low-latency communication. To fulfill these stringent requirements, 5G networks employ re-transmission within the Radio Access Network (RAN) in the event of packet loss, but at the expense of increased latency. An alternative approach to address this challenge involves leveraging Random Linear Network Coding (RLNC) to recover lost packets, thereby eliminating the necessity for retransmission within the RAN. We demonstrate the ability of in-network computing to run RLNC, particularly with the use of RLNC recoder, to enhance reliability and reduce latency within the network.