Streamlining Network Management with Automation

Author(s)	Siva Kumar Mamillapalli
Country	United States
Abstract	The increasing complexity of modern networks renders manual management and incident response unsustainable. This paper investigates how artificial intelligence (AI), machine learning (ML), and software-defined networking (SDN) can automate these critical functions. By leveraging AI/ML for proactive configuration, monitoring, and maintenance, and utilizing SDN for centralized control and scalability, networks can achieve significant improvements in efficiency, security, and performance. Automation also enables real-time threat detection and mitigation, dramatically shortening incident response times. While challenges like data privacy and false positives exist, and future advancements like quantum computing and blockchain are anticipated, the benefits of automated network management and incident response – enhanced efficiency, security, and cost reduction – are undeniable, paving the way for more robust and secure network infrastructures.
Keywords	Network Management, Network Automation, Network Performance, AI/ML, SDN, Incident Response
Field	Engineering
Published In	Volume 1, Issue 4, December 2020
Published On	2020-12-04
Cite This	Streamlining Network Management with Automation - Siva Kumar Mamillapalli - IJLRP Volume 1, Issue 4, December 2020. DOI 10.5281/zenodo.14982469
DOI	https://doi.org/10.5281/zenodo.14982469
Short DOI	https://doi.org/g86879

About IJLRP Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijlrp.com

International Journal of Leading Research Publication