The Role of AI and Machine Learning in Enhancing SD-WAN Performance
Main Article Content
Abstract
Enterprise networks have grown more complex and cloud-based applications have become rampant to the extent that the traditional wide area network (WAN) architectures have been particularly strained. The problem of performance optimization, instant decision-making, and dynamic traffic management has continued to exist despite the introduction of Software-Defined Wide Area Networks (SD-WAN) as a flexible mix. In this paper, we presented the nature of partnership between Artificial Intelligence (AI) and Machine Learning (ML) techniques and SD-WAN architectures in order to assist in overcoming these limitations. Particularly, it focuses on the potential of supervised and reinforcement learning model to improve the task of traffic routing, forecast and neutralize network anomalies, and automated policy statements in accordance with the current network state. Using comparative analysis to counter AI-enhanced SD-WAN systems and traditional rule-based systems, the study shows improvement in several important performance measures that include latency, jitter, packet misplaced, and service level agreement (SLA) adherence. There is also a section in the paper that deals with the implementation challenges such as data privacy, model shift, and non-standardization of vendor platforms. The results paint the disruptive possibilities of AI and ML in achieving smarter, flexible, and robust SD-WAN infrastructure that can support the changing enterprise requirements.
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Sunkara, G. (2022). The Role of AI and Machine Learning in Enhancing SD-WAN Performance. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(04), 1-9. https://doi.org/10.18090/samriddhi.v14i04.34
Section
Research Article
References
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Gupta, A., & Kotaiah, B. (2022). AI and Machine Learning Enabled
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Computing: Proceedings of 5th ICICC 2021, Volume 1 (pp. 131-144).
Singapore: Springer Nature Singapore.
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Infrastructure Implemented Using Cisco System Technologies.
B., & Amoo, O. O. (2021). The future of SD-WAN: A conceptual
evolution from traditional WAN to autonomous, self-healing
network systems. Magna Scientia Advanced Research and
Reviews.
[2] Asif, R., & Ghanem, K. (2021, January). AI secured SD-WAN
architecture as a latency critical IoT enabler for 5G and
beyond communications. In 2021 IEEE 18th Annual Consumer
Communications & Networking Conference (CCNC) (pp. 1-6). IEEE.
[3] Kytömäki, J. (2021). Artificial intelligence and machine learning
with SD-WAN. Technology.
[4] Troia, S., Sapienza, F., Varé, L., & Maier, G. (2020). On deep
reinforcement learning for traffic engineering in SD-WAN. IEEE
Journal on Selected Areas in Communications, 39(7), 2198-2212.
[5] James, T., & Olivia, B. (2020). Optimizing Network Security and
Performance with SD-WAN: Next-Generation Solutions for Modern Enterprises. International Journal of Trend in Scientific
Research and Development, 4(4), 1891-1897.
[6] Qin, Z. (2022). SD-WAN for Bandwidth and Delay Improvements
on the Internet. In SHS Web of Conferences (Vol. 144, p. 02004).
EDP Sciences.
[7] Sirangi, Arjun. (2018). Retail Fraud Detection via Log Analysis
and Stream Processing. Computer Fraud & Security Bulletin.
2018. 21-32. 10.52710/cfs.678.
[8] Cherukupalle, Naga Subrahmanyam. (2018). Declarative IPAM
and DNS Lifecycle Automation in Hybrid Environments Using
Infoblox NIOS and Terraform. Journal of Electrical Systems. 2023.
592-606. 10.5281/zenodo.15723361.
[9] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram.
(2019). Software-Centric Automation Frameworks Integrating
AI and Cybersecurity Principles. International Journal of
Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[10] Jakkaraju, Venkata Thej Deep. (2019). Autonomous Security
Agents for Real-Time IAM Policy Hardening in Multi-Cloud
DevOps Pipelines. Computer Fraud & Security. 2019. 1-9.
[11] Cherukupalle, Naga Subrahmanyam. (2019). Regulatory-Aware
Terraform Modules for Multi-Cloud Infrastructure Provisioning
Across VMware and AWS. Computer Fraud & Security. 2019.
20-31.
[12] Sirangi, Arjun. (2019). Customer Lifetime Value Modelling
with Gradient Boosting. Journal of Information Systems
Engineering & Management. 4. 1-15. 10.52783/jisem.v4i1.6.
[13] Jakkaraju, Venkata Thej Deep. (2020). Adversarial-Aware
Kubernetes Admission Controllers for Real- Time Threat
Suppression. International Journal of Intelligent Systems and
Applications in Engineering. 8. 143-151.
[14] Cherukupalle, Naga Subrahmanyam. (2020). Policy-Based SAN
Zoning Automation using Terraform and Ansible for Cisco
MDS and Brocade Fabrics. International Journal of Intelligent
Systems and Applications in Engineering. 8. 346-357.
[15] Sirangi, Arjun. (2020). Federated Learning for Cross-Brand
Identity Resolution. Computer Fraud & Security Bulletin. 2021.
20-31. 10.52710/cfs.679.
[16] Sirangi, Arjun. (2021). AI-Driven Risk Scoring Engine for
Financial Compliance in Multi-Cloud Environments. Journal
of Electrical Systems. 17. 138-150. 10.52783/jes.8887.
[17] Cherukupalle, Naga Subrahmanyam. (2021). Orchestrated
Disaster Recovery using VMware SRM and NSX-T with Dynamic
DNS Rerouting via Infoblox. International Journal on Recent
and Innovation Trends in Computing and Communication.
9. 26-35.
[18] Akinnagbe, Olayiwola. (2021). Quantum-Resistant Federated
Learning Protocol with Secure Aggregation for Cross-
Border Fraud Detection. International Journal of Computer
Applications Technology and Research. 10. 364-370. 10.7753/
IJCATR1012.1010.
[19] Jakkaraju, A. (2022). International Journal of Communication
Networks and Information Security. International Journal of
Communication Networks and Information Security (June 30,
2022).
[20] Sirangi, Arjun. (2022). Cross-Modal AI for Toxicity Detection in
Product Reviews. Journal of Information Systems Engineering
& Management. 7. 1-11. 10.52783/jisem.v7i1.5.
[21] Jakkaraju, Venkata Thej Deep. (2022). Homomorphic Encryption-
Driven CI/CD Pipelines for Zero- Trust Builds. International
Journal of Communication Networks and Information Security.
14. 1129-1139.
[22] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021).
Autonomous Manufacturing Operations Using Intelligent MES
and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[23] Next-Gen Life Sciences Manufacturing: A Scalable Framework
for AI-Augmented MES and RPA-Driven Precision Healthcare
Solutions. (2023). International Journal of Engineering &
Extended Technologies Research (IJEETR), 5(2), 6275-6281.
https://doi.org/10.15662/IJEETR.2023.0502004
[24] Venkata Krishna Bharadwaj Parasaram. (2021). Explainable
Machine Learning Models for Improving Decision Making in
Project Portfolio Management. Darpan International Research
Analysis, 9(1), 12–21. https://doi.org/10.36676/dira.v9.i1.188
[25] Cherukupalle, Naga Subrahmanyam. (2022). Cross-Site SDDC
Connectivity Using VXLAN and Cisco Unified Fabric for
VCF-Based Infrastructure. Journal of Information Systems
Engineering & Management. 7. 1-12. 10.52783/jisem.v7i4.7.
[26] Sirangi, Arjun. (2022). Ethical Guardrails for Real-Time Generative
Targeting Guardrails. Journal of Electrical Systems. 18. 162-172.
10.52783/jes.8819.
[27] Cherukupalle, Naga Subrahmanyam. (2022). VMware
Cloud Foundation as a Catalyst for AI-Driven Datacentre
Modernization: Optimizing Hybrid Workload by Orchestration
with Edge Computing Integration. International Journal of
Computer Network and Information Security. 14. 1140-1153.
[28] Wairagade, A. (2021). Role of Middleware, Integration Platforms,
and API Solutions in Driving Digital Transformation for
Enterprises. Journal of Science & Technology, 2(1), 387-403.
[29] Satheesh, K. K., Janani, M., Venkateswarlu, S. C., Kumar, R. G.,
Gupta, A., & Kotaiah, B. (2022). AI and Machine Learning Enabled
Software Defined Networks. In Data Engineering and Intelligent
Computing: Proceedings of 5th ICICC 2021, Volume 1 (pp. 131-144).
Singapore: Springer Nature Singapore.
[30] Moser, G. (2021). Performance Analysis of an SD-WAN
Infrastructure Implemented Using Cisco System Technologies.