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On the Benefits of Coding for Network Slicing

Homa Esfahanizadeh, Vipindev Adat Vasudevan, Benjamin D. Kim, Shruti Siva, Jennifer Kim, Alejandro Cohen, Muriel Médard

TL;DR

The study addresses delivering heterogeneous 5G needs by slicing a shared network and comparing un-coded SR-ARQ with coded RLNC under a multi-path binary erasure model. Through analytical derivations of delay and goodput and real-time simulations, it shows that RLNC reduces per-slice resource demands and enables more applications, particularly for URLLC, while also enabling efficient mixed-slice deployments. A hybrid approach—coding selectively in some slices—can smooth the transition to coded networks and lower costs across slices. The work highlights the practical value of incorporating coding-aware decisions into network-slicing strategies and suggests avenues for SDN-based, self-organized resource management.

Abstract

Network slicing has emerged as an integral concept in 5G, aiming to partition the physical network infrastructure into isolated slices, customized for specific applications. We theoretically formulate the key performance metrics of an application, in terms of goodput and delivery delay, at a cost of network resources in terms of bandwidth. We explore an un-coded communication protocol that uses feedback-based repetitions, and a coded protocol, implementing random linear network coding and using coding-aware acknowledgments. We find that coding reduces the resource demands of a slice to meet the requirements for an application, thereby serving more applications efficiently. Coded slices thus free up resources for other slices, be they coded or not. Based on these results, we propose a hybrid approach, wherein coding is introduced selectively in certain network slices. This approach not only facilitates a smoother transition from un-coded systems to coded systems but also reduces costs across all slices. Theoretical findings in this paper are validated and expanded upon through real-time simulations of the network.

On the Benefits of Coding for Network Slicing

TL;DR

The study addresses delivering heterogeneous 5G needs by slicing a shared network and comparing un-coded SR-ARQ with coded RLNC under a multi-path binary erasure model. Through analytical derivations of delay and goodput and real-time simulations, it shows that RLNC reduces per-slice resource demands and enables more applications, particularly for URLLC, while also enabling efficient mixed-slice deployments. A hybrid approach—coding selectively in some slices—can smooth the transition to coded networks and lower costs across slices. The work highlights the practical value of incorporating coding-aware decisions into network-slicing strategies and suggests avenues for SDN-based, self-organized resource management.

Abstract

Network slicing has emerged as an integral concept in 5G, aiming to partition the physical network infrastructure into isolated slices, customized for specific applications. We theoretically formulate the key performance metrics of an application, in terms of goodput and delivery delay, at a cost of network resources in terms of bandwidth. We explore an un-coded communication protocol that uses feedback-based repetitions, and a coded protocol, implementing random linear network coding and using coding-aware acknowledgments. We find that coding reduces the resource demands of a slice to meet the requirements for an application, thereby serving more applications efficiently. Coded slices thus free up resources for other slices, be they coded or not. Based on these results, we propose a hybrid approach, wherein coding is introduced selectively in certain network slices. This approach not only facilitates a smoother transition from un-coded systems to coded systems but also reduces costs across all slices. Theoretical findings in this paper are validated and expanded upon through real-time simulations of the network.
Paper Structure (8 sections, 4 theorems, 17 equations, 3 figures, 1 table)

This paper contains 8 sections, 4 theorems, 17 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Problem Setting
  3. Theoretical Characterization
  4. Communication Protocol 1 (Un-coded): SR-ARQ
  5. Communication Protocol 2 (Coded): RLNC
  6. Communication-Aware Network Slicing
  7. Real-Time Simulation of a Network with Mixed Slices
  8. Conclusions and Future Work

Key Result

Theorem 1

Average delivery delay and average goodput for the multi-path SR-ARQ communication solution are Here, $\overline{\mathcal{P}_j}=\left(\sum_{p_i\in\mathcal{P}_j} p_i\right)/{|\mathcal{P}_j|}$ is the average erasure probability of the slice.

Figures (3)

  • Figure 1: Heterogeneity in advanced meshed communications with hybrid technologies. The slices, which share the same network infrastructure, can use different communication protocols, and they serve different 5G use cases.
  • Figure 2: Average delivery delay and goodput for different slicing choices and communication protocols, over a network with $10$ heterogeneous links.
  • Figure 3: (left): Average in-order delivery delay (IOD); (right): average completion time, for different slicing choices and communication solutions.

Theorems & Definitions (9)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Corollary 1
  • proof
  • Corollary 2
  • Example 1
  • Example 2