Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration
Modern networks are increasingly complex, necessitating dynamic and automated solutions to connect user intents with network actions effectively. This study presents a new framework for automating Intent Based Networking (IBN) by combining cognitive Machine Reasoning (MR) with Natural Language Proce...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10854217/ |
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| author | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song |
| author_facet | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song |
| author_sort | Muhammad Asif |
| collection | DOAJ |
| description | Modern networks are increasingly complex, necessitating dynamic and automated solutions to connect user intents with network actions effectively. This study presents a new framework for automating Intent Based Networking (IBN) by combining cognitive Machine Reasoning (MR) with Natural Language Processing (NLP) and utilizing the RASA (Robust Automated Speech Assistant) architecture. RASA is a flexible open-source framework for building conversational AI, adapted for end-to-end (e2e) network orchestration. In contrast to traditional static methods, this innovative system empowers network operators to manage and optimize networks dynamically through intuitive voice commands or a Graphical User Interface (GUI). The system identifies user intents, converts them into actionable network policies, and ensures they align with real-time network states and Quality of Service (QoS) requirements via a feedback loop. Cognitive MR and AI-based optimization techniques are integrated to enhance system performance, enabling intelligent adaptation to network conditions and ensuring optimal resource allocation. A simulated testbed was created to assess the system’s performance using Containernet, a lightweight Container-Based Network Emulator, and Open Networking Operating System (ONOS) Software Defined Networking (SDN) controllers. The results of the testbed indicated a 25% reduction in latency, a 30% increase in throughput, and a 40% enhancement in real-time response times, demonstrating the system’s effectiveness in a controlled environment. These impressive results underscore the system’s potential to enhance network performance, efficiency, and responsiveness. By effectively addressing modern networks’ challenges, this solution proves its ability to confidently and seamlessly convert user intents into automated network actions without manual intervention, providing adaptability and scalability for today’s network environments. |
| format | Article |
| id | doaj-art-1c69efff2ee346a58dc0213a371697e2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1c69efff2ee346a58dc0213a371697e22025-01-31T23:05:25ZengIEEEIEEE Access2169-35362025-01-0113194561946810.1109/ACCESS.2025.353428210854217Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service OrchestrationMuhammad Asif0https://orcid.org/0000-0002-1048-0912Talha Ahmed Khan1https://orcid.org/0000-0001-6411-163XWang-Cheol Song2https://orcid.org/0000-0002-7411-5316Department of Computer Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of KoreaInstitute for Communication Systems, University of Surrey, Guildford, U.K.Department of Computer Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of KoreaModern networks are increasingly complex, necessitating dynamic and automated solutions to connect user intents with network actions effectively. This study presents a new framework for automating Intent Based Networking (IBN) by combining cognitive Machine Reasoning (MR) with Natural Language Processing (NLP) and utilizing the RASA (Robust Automated Speech Assistant) architecture. RASA is a flexible open-source framework for building conversational AI, adapted for end-to-end (e2e) network orchestration. In contrast to traditional static methods, this innovative system empowers network operators to manage and optimize networks dynamically through intuitive voice commands or a Graphical User Interface (GUI). The system identifies user intents, converts them into actionable network policies, and ensures they align with real-time network states and Quality of Service (QoS) requirements via a feedback loop. Cognitive MR and AI-based optimization techniques are integrated to enhance system performance, enabling intelligent adaptation to network conditions and ensuring optimal resource allocation. A simulated testbed was created to assess the system’s performance using Containernet, a lightweight Container-Based Network Emulator, and Open Networking Operating System (ONOS) Software Defined Networking (SDN) controllers. The results of the testbed indicated a 25% reduction in latency, a 30% increase in throughput, and a 40% enhancement in real-time response times, demonstrating the system’s effectiveness in a controlled environment. These impressive results underscore the system’s potential to enhance network performance, efficiency, and responsiveness. By effectively addressing modern networks’ challenges, this solution proves its ability to confidently and seamlessly convert user intents into automated network actions without manual intervention, providing adaptability and scalability for today’s network environments.https://ieeexplore.ieee.org/document/10854217/Intent based networkingnatural language processingmachine reasoningsoftware defined networkingconversational AI |
| spellingShingle | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration IEEE Access Intent based networking natural language processing machine reasoning software defined networking conversational AI |
| title | Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration |
| title_full | Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration |
| title_fullStr | Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration |
| title_full_unstemmed | Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration |
| title_short | Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration |
| title_sort | leveraging cognitive machine reasoning and nlp for automated intent based networking and e2e service orchestration |
| topic | Intent based networking natural language processing machine reasoning software defined networking conversational AI |
| url | https://ieeexplore.ieee.org/document/10854217/ |
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