Research implementation of a Cognitive Extension of the Autonomic Networking Integrated Model and Approach (ANIMA) using Agentic Artificial Intelligence for intelligent, autonomous, and context-aware network management.
Modern computer networks have evolved from manually configured infrastructures to programmable and self-managing systems. While the Autonomic Networking Integrated Model and Approach (ANIMA) introduced autonomic networking through Autonomic Service Agents (ASAs), existing implementations remain primarily rule-based and lack intelligent reasoning, adaptive planning, semantic collaboration, and contextual decision-making.
This research proposes a Cognitive Autonomic Service Agent (C-ASA) that extends traditional ANIMA by integrating Agentic Artificial Intelligence, Large Language Models (LLMs), and Semantic Agent-to-Agent (A2A) Communication to enable autonomous, collaborative, and intelligent network management.
This repository serves as the official implementation, experimental laboratory, and documentation supporting the undergraduate research.
- Design a Cognitive Extension of ANIMA
- Develop the Cognitive Autonomic Service Agent (C-ASA)
- Study the evolution of network management
- Understand Traditional Networking
- Understand Software Defined Networking (SDN)
- Explore Autonomic Networking concepts
- Integrate Agentic AI into network management
- Implement Semantic Agent-to-Agent Communication
- Evaluate intelligent autonomous decision making
- Compare Traditional Networks, SDN, ANIMA and C-ASA
This work combines multiple research disciplines.
- Computer Networks
- Software Defined Networking (SDN)
- Autonomic Networking (ANIMA)
- Artificial Intelligence
- Agentic AI
- Large Language Models
- Multi-Agent Systems
- Distributed Systems
- Network Automation
- Cyber Security
The implementation follows the historical evolution of networking technologies.
Traditional Networking
│
▼
Network Automation
│
▼
Software Defined Networking (SDN)
│
▼
Autonomic Networking (ANIMA)
│
▼
Cognitive Networking
│
▼
Cognitive Autonomic Service Agent (C-ASA)
Each phase introduces new capabilities that eventually lead to the proposed Cognitive ASA architecture.
The implementation is divided into five progressive phases.
- Traditional Network Architecture
- Static Routing
- Dynamic Routing (OSPF)
- Network Performance Analysis
- Python Network Automation
- Remote Device Configuration
- Automated Network Management
- Open vSwitch
- OpenFlow
- SDN Architecture
- Ryu Controller
- Flow Rule Management
- Policy-Based Networking
- ANIMA Concepts
- Autonomic Service Agent (ASA)
- Agent Discovery
- Policy Negotiation
- Cognitive Autonomic Service Agent (C-ASA)
- Agentic AI Integration
- Semantic Agent Communication
- Risk-Aware Decision Making
- Intelligent Policy Reasoning
- Experimental Evaluation
The research experimentally evaluates:
- Traditional Enterprise Networks
- Routing and Switching
- Network Automation
- Software Defined Networking
- OpenFlow Control
- Autonomic Networking
- Autonomic Service Agents
- Cognitive Service Agents
- Semantic Agent Communication
- Autonomous Decision Making
- Performance Comparison
- GNS3
- Mininet
- Cisco IOS
- Open vSwitch
- FRRouting
- OpenFlow
- Bridge Utilities
- iptables
- Python 3
- Git
- pip
- build-essential
- Paramiko
- Netmiko
- REST APIs
- OpenFlow
- Ryu Controller
- Wireshark
- tcpdump
- traceroute
- nmap
- iperf3
- Large Language Models (LLMs)
- Agentic AI
- Semantic Agent Communication
- Multi-Agent Systems
The proposed framework introduces:
- Cognitive Autonomic Service Agent (C-ASA)
- AI-Driven Network Reasoning
- Semantic Agent-to-Agent Communication
- Autonomous Planning Engine
- Intelligent Policy Negotiation
- Context-Aware Networking
- Risk-Aware Decision Engine
- Agentic AI for Network Operations
- Cognitive Extension of ANIMA
Current progress includes:
- Literature Review
- Traditional Networking
- Software Defined Networking
- ANIMA Study
- Autonomic Service Agent Analysis
- Laboratory Environment
- Experimental Design
- Cognitive ASA Architecture
Upcoming work:
- C-ASA Implementation
- Semantic Communication Layer
- Experimental Evaluation
- Performance Analysis
- Thesis Completion
Future extensions include:
- Multi-Controller SDN
- Distributed Cognitive Agents
- Reinforcement Learning
- Intent-Based Networking
- Zero Trust Integration
- Cloud-Native Deployment
- Kubernetes Networking
- Edge Computing
- Digital Twin Networks
If this repository contributes to your research, please cite the associated thesis after publication.
Ashrith Peechara
"Cognitive Autonomic Networking:
Extending ANIMA Through Agentic AI and Semantic Agent-to-Agent Communication"
Bachelor of Technology Thesis
Department of Cyber Security
This repository is released under the MIT License.
The implementation is intended for educational and research purposes.
The associated thesis, figures, documentation, and written content remain the intellectual property of the author unless explicitly stated otherwise.
Ashrith Peechara
Cyber Security Researcher
Bachelor of Technology (Cyber Security)
This research is conducted as part of an undergraduate research project exploring the integration of Artificial Intelligence with autonomic networking architectures for next-generation autonomous network management.
"From programmable networks to cognitive networks — enabling autonomous infrastructures through intelligent reasoning and collaborative agents."