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NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems.

Angela Rodriguez-Vivas1, Oscar Mauricio Caicedo1, Armando Ordoñez1

  • 1Department of Telematics, University of Cauca, Popayán 190002, Colombia.

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NORA automatically generates automated planning problems from natural language policies, simplifying self-driving network management. This approach overcomes barriers for non-AI experts in implementing autonomic control loops.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Management

Background:

  • Autonomic management control loops are essential for self-driving networks.
  • Automated Planning (AP) shows feasibility for these control loops.
  • Current AP implementation is complex for non-AI expert network administrators.

Purpose of the Study:

  • To introduce NORA, a novel approach for automatically generating AP-problems.
  • To translate natural language Goal Policies into AP-goals for network management.
  • To overcome the challenge of using natural language policies in AP-based autonomic management.

Main Methods:

  • NORA utilizes Natural Language Processing (NLP) to translate Goal Policies into AP-goals.
  • Templates are employed to combine translated goals with network status and tasks.
  • A prototype was evaluated using a dataset of Goal Policies.

Main Results:

  • NORA successfully generates AP-problems from natural language policies.
  • The system demonstrates high precision in translation and problem generation.
  • NORA significantly reduces the time required for AP-problem creation.

Conclusions:

  • NORA effectively bridges the gap between natural language policies and AP requirements.
  • The approach lowers the barrier for network administrators to adopt AP in autonomic management.
  • NORA facilitates the realization of self-driving networks through simplified AP integration.