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Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks

Osel Lhamo, Tung V. Doan, Elif Tasdemir, Mahdi Attawna, Giang T. Nguyen, Patrick Seeling, Martin Reisslein, Frank H. P. Fitzek

TL;DR

The paper tackles reliability and latency challenges in cloud-native 5G networks by introducing two programmable RLNC-based solutions, FlexNC and RecNet. FlexNC provides a flexible framework to fuse SBC and SW RLNC protocols with in-band signaling, enabling traffic-type aware coding that can balance latency and reliability. RecNet extends coding into the network via a recoder at relay nodes to further reduce packet loss for remote UEs, demonstrated on real hardware with OpenAirInterface and dockerized components. Across diverse traffic (video, haptic, audio) and noise models (random and bursty), FlexNC halved or better the latency for several streams while maintaining reliability, and RecNet achieved substantial packet-loss reductions with minimal delay overhead, validating practical RLNC deployment in cloud-native 5G systems.

Abstract

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 solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC.

Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks

TL;DR

The paper tackles reliability and latency challenges in cloud-native 5G networks by introducing two programmable RLNC-based solutions, FlexNC and RecNet. FlexNC provides a flexible framework to fuse SBC and SW RLNC protocols with in-band signaling, enabling traffic-type aware coding that can balance latency and reliability. RecNet extends coding into the network via a recoder at relay nodes to further reduce packet loss for remote UEs, demonstrated on real hardware with OpenAirInterface and dockerized components. Across diverse traffic (video, haptic, audio) and noise models (random and bursty), FlexNC halved or better the latency for several streams while maintaining reliability, and RecNet achieved substantial packet-loss reductions with minimal delay overhead, validating practical RLNC deployment in cloud-native 5G systems.

Abstract

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 solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC.
Paper Structure (52 sections, 2 equations, 14 figures, 1 table, 5 algorithms)

This paper contains 52 sections, 2 equations, 14 figures, 1 table, 5 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. 5G Network Architecture
  4. Cloud-native 5G
  5. General Reliability in 5G System
  6. Retransmissions (ARQ)
  7. Forward Error Corrections (FEC)
  8. Other Approaches
  9. Network Coding Reliability for 5G Systems
  10. System Model
  11. Programmable Network Coding: FlexNC
  12. FlexNC with SBC
  13. Encoding in FlexNC-SBC
  14. Decoding in FlexNC-SBC
  15. FlexNC with SW
  16. ...and 37 more sections

Figures (14)

  • Figure 1: Structure of the paper
  • Figure 2: System model for the proposed algorithm: FlexNC
  • Figure 3: System model for the proposed algorithm: RecNet
  • Figure 4: Basic example of Random Linear Network Coding in RecNet
  • Figure 5: Testbed architecture for the integration of RLNC into the OAI. All four host computers have an Intel Core i7-6700 (3.40GHz) CPU, 32GB RAM and Ubuntu 18.04 OS.
  • ...and 9 more figures