Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Alternative paths computation for congestion mitigation in segment-routing networks

Sébastien Martin, Youcef Magnouche, Paolo Medagliani, Jérémie Leguay

TL;DR

The paper addresses pre-computation of traffic-diverting alternative paths for SR-based congestion mitigation, focusing on resilience to any single link failure. It shows that maximizing the worst-case reroutable flow (APCP) is NP-hard and presents a Benders-decomposition approach, alongside a scalable relaxation (RAPCP) that maximizes the number of surviving disjoint paths and the minimum path capacity via min-cost-flow techniques. Experiments reveal a tradeoff: APCP yields higher post-failure throughput but is computationally intensive, while RAPCP provides fast, scalable solutions with stronger resilience metrics (more surviving/disjoint paths) albeit at potentially lower flow capacity after failure. Collectively, the work offers both exact and scalable strategies to improve congestion mitigation in SR networks, with practical implications for backbone operators seeking distributed, fail-safe rerouting mechanisms under dynamic traffic conditions.

Abstract

In backbone networks, it is fundamental to quickly protect traffic against any unexpected event, such as failures or congestions, which may impact Quality of Service (QoS). Standard solutions based on Segment Routing (SR), such as Topology-Independent Loop-Free Alternate (TI-LFA), are used in practice to handle failures, but no distributed solutions exist for distributed and tactical congestion mitigation. A promising approach leveraging SR has been recently proposed to quickly steer traffic away from congested links over alternative paths. As the pre-computation of alternative paths plays a paramount role to efficiently mitigating congestions, we investigate the associated path computation problem aiming at maximizing the amount of traffic that can be rerouted as well as the resilience against any 1-link failure. In particular, we focus on two variants of this problem. First, we maximize the residual flow after all possible failures. We show that the problem is NP-Hard, and we solve it via a Benders decomposition algorithm. Then, to provide a practical and scalable solution, we solve a relaxed variant problem, that maximizes, instead of flow, the number of surviving alternative paths after all possible failures. We provide a polynomial algorithm. Through numerical experiments, we compare the two variants and show that they allow to increase the amount of rerouted traffic and the resiliency of the network after any 1-link failure.

Alternative paths computation for congestion mitigation in segment-routing networks

TL;DR

The paper addresses pre-computation of traffic-diverting alternative paths for SR-based congestion mitigation, focusing on resilience to any single link failure. It shows that maximizing the worst-case reroutable flow (APCP) is NP-hard and presents a Benders-decomposition approach, alongside a scalable relaxation (RAPCP) that maximizes the number of surviving disjoint paths and the minimum path capacity via min-cost-flow techniques. Experiments reveal a tradeoff: APCP yields higher post-failure throughput but is computationally intensive, while RAPCP provides fast, scalable solutions with stronger resilience metrics (more surviving/disjoint paths) albeit at potentially lower flow capacity after failure. Collectively, the work offers both exact and scalable strategies to improve congestion mitigation in SR networks, with practical implications for backbone operators seeking distributed, fail-safe rerouting mechanisms under dynamic traffic conditions.

Abstract

In backbone networks, it is fundamental to quickly protect traffic against any unexpected event, such as failures or congestions, which may impact Quality of Service (QoS). Standard solutions based on Segment Routing (SR), such as Topology-Independent Loop-Free Alternate (TI-LFA), are used in practice to handle failures, but no distributed solutions exist for distributed and tactical congestion mitigation. A promising approach leveraging SR has been recently proposed to quickly steer traffic away from congested links over alternative paths. As the pre-computation of alternative paths plays a paramount role to efficiently mitigating congestions, we investigate the associated path computation problem aiming at maximizing the amount of traffic that can be rerouted as well as the resilience against any 1-link failure. In particular, we focus on two variants of this problem. First, we maximize the residual flow after all possible failures. We show that the problem is NP-Hard, and we solve it via a Benders decomposition algorithm. Then, to provide a practical and scalable solution, we solve a relaxed variant problem, that maximizes, instead of flow, the number of surviving alternative paths after all possible failures. We provide a polynomial algorithm. Through numerical experiments, we compare the two variants and show that they allow to increase the amount of rerouted traffic and the resiliency of the network after any 1-link failure.
Paper Structure (11 sections, 3 theorems, 4 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 3 theorems, 4 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Congestion Mitigation
  4. Alternative paths' computation
  5. Compact formulation
  6. Benders model
  7. Sub-problem
  8. Master problem
  9. Relaxation of the problem
  10. Experimental results
  11. Conclusion

Key Result

Theorem 1

The alternative paths' computation problem, already by only considering objective 1), is NP-hard, even for $k=3$.

Figures (4)

  • Figure 1: Example of solution for APCP with $k=3$
  • Figure 2: Example of solution for APCP (green, yellow, and red paths) and RAPCP (red, purple, and green paths) where $k=3$.
  • Figure 3: Performance Profile comparing the CPU time on Configuration 1 (Left) and Configuration 2 (Right) instances. RAPCP and RAPCP2a are not visible since they reach 100% in less than a second.
  • Figure 4: Performance Profile comparing the max-flow after 1 link failure on Configuration 1 (Left) and Configuration 2 (Right) instances. RAPCP is not visible, overlapped by RAPCPa2 curves. Similarly for APCP in the left plot.

Theorems & Definitions (5)

  • Theorem 1
  • proof
  • Proposition 1
  • Proposition 2
  • proof