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Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles.

Tuan-Minh Pham1,2, Thi-Minh Nguyen3

  • 1Faculty of Computer Science, Phenikaa University, Hanoi 12116, Vietnam.

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Summary
This summary is machine-generated.

Connected autonomous vehicles generate vast data, impacting performance. Network Function Virtualization (NFV) offers solutions, but VNF failures pose challenges. This study presents methods to minimize service disruption in NFV-based CAV systems.

Keywords:
NFVVCAVconnected autonomous vehiclesoptimizationreinforcement learningresilient service

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Connected autonomous vehicles (CAVs) generate massive data, straining processing and transfer capabilities.
  • Network Function Virtualization (NFV) offers a flexible, low-latency solution for CAV services by deploying Virtual Network Functions (VNFs) on edge and cloud servers.
  • Ensuring service resilience against VNF failures in NFV-based CAV (VCAV) systems is a critical challenge.

Purpose of the Study:

  • To develop methods for minimizing service disruption costs in VCAV systems during VNF failures.
  • To provide optimal and approximate solutions for resilient VNF placement and dynamic routing in VCAV systems.
  • To evaluate the effectiveness of proposed resilience strategies in realistic VCAV environments.

Main Methods:

  • An integer linear programming (ILP) model, TERO, was developed for optimal VNF placement and dynamic routing to ensure service resilience.
  • Two approximation algorithms, TERH (heuristic-based) and TERA (reinforcement learning-based), were proposed for large-scale VCAV systems.
  • The performance of TERO, TERH, and TERA was evaluated using real datasets and generated network topologies.

Main Results:

  • TERO provides an optimal solution for traffic engineering, VNF placement, and routing for resilient services.
  • TERH and TERA offer near-optimal approximation solutions for resilient services in large-scale VCAV systems.
  • TERA demonstrates particular suitability for highly dynamic VCAV environments.

Conclusions:

  • The proposed ILP model (TERO) and approximation algorithms (TERH, TERA) effectively address service resilience challenges in VCAV systems.
  • TERH and TERA provide efficient and scalable solutions for minimizing service disruption costs.
  • TERA is recommended for highly dynamic VCAV systems due to its adaptive learning capabilities.