AI-POWERED NETWORK AUTOMATION: THE NEXT FRONTIER IN NETWORK MANAGEMENT
Keywords:
AI-Powered Network Automation, Predictive Network Management, Self-Optimizing Networks, Self-Healing Networks, Future Trends, Challenges In AI-Driven NetworkingAbstract
The rapid growth of computer networks has led to increased complexity in network management, making traditional manual approaches inefficient and error-prone. Artificial Intelligence (AI) has emerged as a promising solution to streamline network management processes and improve overall network performance. This article explores the transformative potential of AI in network automation, focusing on its ability to predict and prevent network issues, optimize network resources, and enable self-healing capabilities. We present real-world case studies and data-driven insights to demonstrate the effectiveness of AI in enhancing network reliability, reducing downtime, and minimizing human intervention. By leveraging AI techniques such as machine learning, deep learning, and reinforcement learning, network administrators can proactively address network challenges and ensure optimal network performance. The article concludes by discussing the future trends and challenges in AI-powered network automation, highlighting the need for standardization, security, and collaboration among stakeholders.
References
Cisco, "Cisco Visual Networking Index: Forecast and Trends, 2017-2022," White Paper, Feb. 2019.
S. Ayoubi, N. Limam, M. A. Salahuddin, N. Shahriar, R. Boutaba, F. Estrada-Solano, and O. M. Caicedo, "Machine Learning for Cognitive Network Management," IEEE Communications Magazine, vol. 56, no. 1, pp. 158-165, Jan. 2018.
Gartner, "The Cost of Downtime," 2014. [Online]. Available: https://blogs.gartner.com/andrew-lerner/2014/07/16/the-cost-of-downtime/
R. Boutaba , "A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications, and Research Opportunities," Journal of Internet Services and Applications, vol. 9, no. 1, p. 16, Jun. 2018.
Opengear, "Measuring the True Cost of Network Outages," 2020. [Online]. Available: https://opengear.com/white-paper/measuring-true-cost-network-outages
Z. M. Fadlullah, "State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems," IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, 2017.
B. Mao, F. Tang, Z. M. Fadlullah, and N. Kato, "A Novel Non-Supervised Deep Learning Based Network Traffic Control Method for Software Defined Wireless Networks," IEEE Wireless Communications, vol. 25, no. 4, pp. 74-81, Aug. 2018.
IDC, "IDC FutureScape: Worldwide Future of Connectedness 2020 Predictions," Oct. 2019.
N. Kato, "The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective," IEEE Wireless Communications, vol. 24, no. 3, pp. 146-153, Jun. 2017.
Cisco, "Cisco DNA Center: Intent-Based Networking for the Enterprise," Solution Overview, 2020.
Juniper Networks, "Marvis: AI-Driven Virtual Network Assistant," Datasheet, 2021.
Accenture, "Reinventing Networks with Artificial Intelligence," 2019. [Online]. Available: https://www.accenture.com/_acnmedia/PDF-107/Accenture-Reinventing-Networks-with-AI.pdf
J. Xie , "A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 393-430, 2019.
T. Taleb, "On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration," IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1657-1681, 2017.
Gartner, "The Cost of Downtime," 2014. [Online]. Available: https://blogs.gartner.com/andrew-lerner/2014/07/16/the-cost-of-downtime/
M. Wang, Y. Cui, X. Wang, S. Xiao, and J. Jiang, "Machine Learning for Networking: Workflow, Advances, and Opportunities," IEEE Network, vol. 32, no. 2, pp. 92-99, Mar. 2018.
Cisco, "Predictive Network Analytics: Capturing the Value of Machine Learning," White Paper, 2019.
Cisco, "Cisco DNA Center: Intent-Based Networking for the Enterprise," Solution Overview, 2020.
Cisco, "Cisco DNA Assurance: Deliver Proactive Network Management with AI/ML," Solution Brief, 2021.
Cisco, "Global Financial Services Company Improves Network Uptime and Performance with Cisco DNA Center," Case Study, 2020.
Cisco, "Cisco DNA Center: Assurance and Analytics," Datasheet, 2021.
Juniper Networks, "Marvis: AI-Driven Virtual Network Assistant," Datasheet, 2021.
Juniper Networks, "Global Retailer Achieves Network Efficiency and Uptime with Juniper Marvis," Case Study, 2021.
S. Yoo, J. Song, and H. Kim, "Proactive Network Maintenance Using Machine Learning and Data Mining Techniques," in 2019 International Conference on Information Networking (ICOIN), 2019, pp. 188-193.
IDC, "Worldwide Semiannual Artificial Intelligence Tracker," 2021. [Online]. Available: https://www.idc.com/getdoc.jsp?containerId=prUS47482321
H. Mao, M. Alizadeh, I. Menache, and S. Kandula, "Resource Management with Deep Reinforcement Learning," in Proceedings of the 15th ACM Workshop on Hot Topics in Networks, 2016, pp. 50-56.
Ericsson, "Mobility Report," Nov. 2020. [Online]. Available: https://www.ericsson.com/en/mobility-report
S. Ayoubi, N. Limam, M. A. Salahuddin, N. Shahriar, R. Boutaba, F. Estrada-Solano, and O. M. Caicedo, "Machine Learning for Cognitive Network Management," IEEE Communications Magazine, vol. 56, no. 1, pp. 158-165, Jan. 2018.
IEEE, "The Role of AI in Network Optimization," IEEE Network, vol. 34, no. 4, pp. 8-15, Jul. 2020.
AT&T, "ECOMP (Enhanced Control, Orchestration, Management, and Policy) Architecture White Paper," White Paper, 2016.
AT&T, "Reinventing the Network with ECOMP," White Paper, 2017.
AT&T, "AT&T Builds on 5G Momentum with Next-Gen AI-Powered Network Optimization," Press Release, 2021.
Huawei, "iMaster NCE: Autonomous Driving Networks with AI," White Paper, 2020.
Huawei, "European Operator Boosts 5G Network Performance with Huawei iMaster NCE," Case Study, 2021.
Huawei, "iMaster NCE: Enabling Intelligent 5G Networks," Datasheet, 2021.
Z. Fadlullah, "State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems," IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, 2017.
McKinsey & Company, "The Future of Connectivity: Enabling the Internet of Things," Report, 2019.
T. Zhang, X. Wang, and X. Xu, "Self-Adaptive Network Fault Diagnosis Based on Cross-Layer Analysis," in 2019 IEEE International Conference on Communications (ICC), 2019, pp. 1-6.
Opengear, "Measuring the True Cost of Network Outages," 2020. [Online]. Available: https://opengear.com/white-paper/measuring-true-cost-network-outages
J. Ali-Tolppa, S. Kocsis, B. Schultz, L. Bodrog, and M. Kajo, "Self-Healing and Resilience in Future 5G Cognitive Autonomous Networks," in 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), 2018, pp. 1-8.
Huawei, "iMaster NCE: Autonomous Driving Networks with AI," White Paper, 2020.
Huawei, "Accelerating Network Fault Recovery with Huawei iMaster NCE," Case Study, 2021.
Huawei, "iMaster NCE: Enabling Intelligent Autonomous Networks," Datasheet, 2021.
Nokia, "Nokia Self-Organizing Networks (SON): Intelligent Automation for 5G," White Paper, 2020.
Nokia, "Asian Operator Boosts Network Resilience with Nokia SON," Case Study, 2021.
Nokia, "Nokia SON: Enabling Automated Network Healing," Datasheet, 2021.
S. Yoo, J. Song, and H. Kim, "Proactive Network Maintenance Using Machine Learning and Data Mining Techniques," in 2019 International Conference on Information Networking (ICOIN), 2019, pp. 188-193.
MarketsandMarkets, "Self-Healing Networks Market by Technology, Component, Application, and Region - Global Forecast to 2025," Report, 2020.
X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, "Network Slicing in 5G: Survey and Challenges," IEEE Communications Magazine, vol. 55, no. 5, pp. 94-100, May 2017.
GSMA, "The 5G Era: Age of Boundless Connectivity and Intelligent Automation," Report, 2017.
I. Afolabi, T. Taleb, K. Samdanis, A. Ksentini, and H. Flinck, "Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions," IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2429-2453, 2018.
N. Kato, Z. M. Fadlullah, B. Mao, F. Tang, O. Akashi, and T. Shimizu, "The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective," IEEE Wireless Communications, vol. 24, no. 3, pp. 146-153, Jun. 2017.
IEEE, "Challenges and Opportunities for Artificial Intelligence in Network Management," IEEE Network, vol. 35, no. 1, pp. 6-12, Jan. 2021.
B. Blanco, J. O. Fajardo, I. Giannoulakis, and E. Kafetzakis, "On the Security and Privacy of 5G Multi-Domain Networks: Challenges and Solutions," in 2018 IEEE Conference on Standards for Communications and Networking (CSCN), 2018, pp. 1-6.
M. Usama, J. Qadir, and A. Al-Fuqaha, "Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward," in 2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018, pp. 90-97.
S. Niknam, A. Roy, H. Dhillon, S. Singh, R. Banerji, and J. H. Reed, "Intelligent O-RAN for Beyond 5G and 6G Wireless Networks," IEEE Communications Magazine, vol. 58, no. 7, pp. 21-27, Jul. 2020.
O-RAN Alliance, "O-RAN: Towards an Open and Smart RAN," White Paper, 2018.
O-RAN Alliance, "O-RAN Use Cases and Deployment Scenarios," White Paper, 2020.
ETSI, "Zero-Touch Network and Service Management (ZSM): Reference Architecture," ETSI GS ZSM 002 V1.1.1, Aug. 2019.
ETSI, "Zero-Touch Network and Service Management (ZSM): Landscape," ETSI GR ZSM 001 V1.1.1, Jul. 2020.
M. Usama, M. Asim, J. Qadir, H. Ala-Al-Fuqaha, and M. A. Imran, "Adversarial Machine Learning Attack on MANET," in 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020, pp. 349-354.
N. Kato, B. Mao, F. Tang, Y. Kawamoto, and J. Liu, "Ten Challenges in Advancing Machine Learning Technologies toward 6G," IEEE Wireless Communications, vol. 27, no. 3, pp. 96-103, Jun. 2020.
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Copyright (c) 2024 Chaitanya Kumar Kadiyala , Shashikanth Gangarapu, Sadha Shiva Reddy Chilukoori (Author)
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