A Reference Architecture for Autonomous Networks An Agent-Based Approach

2025-03-17

AI Summary

Autonomous Networks (AN) face critical challenges in architectural scalability and cross-agent coordination consistency as they evolve toward large-scale, unmanned operation. Method: This paper proposes the first systematic multi-agent reference architecture for AN, featuring a hierarchical agent design with rigorously defined functional boundaries and collaboration protocols across layers; it innovatively integrates a domain knowledge graph–driven long-term memory mechanism to ensure decision consistency and co-evolution among agents; and it unifies AI-enabled closed-loop control with scalable modeling techniques. Contribution/Results: The architecture is empirically validated on representative network use cases, demonstrating feasibility and robustness. It establishes a reusable methodological foundation and architectural framework bridging the gap between theoretical AN design and practical engineering deployment, thereby advancing the standardization and industrial adoption of autonomous networking.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The vision of autonomous systems is becoming increasingly important in many application areas, where the aim is to replace humans with agents. These include autonomous vehicles and other agents' applications in business processes and problem-solving. For networks, the increasing scale and operation and management (O&M) complexity drive the need for autonomous networks (AN). The technical objective of AN is to ensure trustworthy O&M without human intervention for higher efficiency and lower operating costs. However, realizing AN seems more difficult than autonomous vehicles. It encounters challenges of networks' structural and functional complexity, which operate as distributed dynamic systems governed by various technical and economic constraints. A key problem lies in formulating a rigorous development methodology that facilitates a seamless transition from traditional networks to AN. Central to this methodology is the definition of a reference architecture for network agents, which specifies the required functionalities for their realization, regardless of implementation choices. This article proposes a reference architecture characterizing main functional features, illustrating its application with network use cases. It shows how artificial intelligence components can be used to implement the required functionality and its coordination. The latter is achieved through the management and generation of shared domain-specific knowledge stored in long-term memory, ensuring the overall consistency of decisions and their execution. The article concludes with a discussion of architecture specialization for building network layer agents. It also identifies the main technical challenges ahead, such as satisfying essential requirements at development or runtime, as well as the issue of coordinating agents to achieve collective intelligence in meeting overall network goals.

Problem

• Develop a reference architecture for autonomous networks.
• Address challenges in transitioning from traditional to autonomous networks.
• Implement AI for agent coordination and decision consistency.

Innovation

• Agent-based reference architecture for autonomous networks.
• AI components for functionality and coordination implementation.
• Shared domain-specific knowledge for decision consistency.