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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Enhancing Network Management Through Machine Learning in Software-Defined Networking |
| Authors Name: | Hemant Kumar Bhardwaj , Arvind Panwar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_220172
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| Published Paper Id: | IJNRD2404880 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | The increasing diversification of Internet applications and the ongoing evolution of network infrastructure, fueled by emerging technologies, have introduced complexities to network management. Effectively classifying network traffic is crucial for managing network resources based on quality of service and security requirements. However, conventional traffic classification methods relying on Deep Packet Inspection fall short of meeting the demanding scalability, security, and privacy criteria. The centralized controller in Software-Defined Networking offers a comprehensive network view, easing traffic analysis and providing direct programming capabilities. This allows for dynamic adjustments of traffic flows to meet evolving network requirements. The integration of Machine Learning techniques, along with these features, enables the infusion of intelligence into networks, optimizing their performance and enhancing management and maintenance. In this context, our work aims to conduct a Systematic Literature Review on traffic classification in Software-Defined Networking using Machine Learning techniques. Additionally, we systematically analyze and organize the chosen seminal works based on the categorization of traffic classes and the employed Machine Learning techniques, drawing meaningful research conclusions. Finally, we identify new challenges and propose future research directions in this domain. |
| Keywords: | SDN (Software-Defined Networking,) Machine Learning Integration, Traffic Classification, Evolution Network Management, Optimization Intelligent |
| Cite Article: | "Enhancing Network Management Through Machine Learning in Software-Defined Networking", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i703-i720, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404880.pdf |
| Downloads: | 00034 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404880 Registration ID: 220172 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i703-i720 Country: GHAZIABAD, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404880 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404880 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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