Implementation for Improving SDN Efficiency with The ML Algorithm, RF

Saroj Singh; Dr. Kamlesh Sharma

doi:10.14419/yxxp2e46

Article Details

Implementation for Improving SDN Efficiency with The ML Algorithm, RF

Authors:

Saroj Singh | Dr. Kamlesh Sharma

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.170-177

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387281

pISSN:

2227-5053

DOI:

10.14419/yxxp2e46

Keywords:

Abstract:

 Software-Defined Networking (SDN) offers unprecedented profitability and centralized control over network infrastructure, yet optimizing ‎its efficiency, particularly in dynamic and complex environments, remains a significant challenge. Traditional network management often ‎struggles with real-time traffic classification and resource allocation, leading to congestion and suboptimal performance. The paper proposes a ‎novel approach to enhance SDN efficiency through the integration of a Random Forest machine learning model for intelligent traffic classi‎fication and proactive resource management. We leverage the Random Forest's ability to handle high-dimensional data, identify complex ‎patterns, and provide robust predictions for various traffic types. Our methodology involves collecting network flow data from an emulated ‎SDN environment, extracting relevant features, training the Random Forest model for accurate traffic categorization (e.g., critical, best-effort, ‎delay-sensitive), and subsequently using these classifications to inform the SDN controller's decisions on dynamic path allocation, load ‎balancing, and Quality of Service (QoS) enforcement. Experimental results demonstrate that the Random Forest model significantly ‎improves network throughput, reduces latency, and enhances overall resource utilization compared to traditional rule-based or less intelligent ‎approaches in SDN. This research contributes to the growing body of knowledge on applying machine learning to optimize modern network architectures‎.

Article Details

Implementation for Improving SDN Efficiency with The ML Algorithm, RF

Authors:

Saroj Singh | Dr. Kamlesh Sharma

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.170-177

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387281

pISSN:

2227-5053

DOI:

10.14419/yxxp2e46

Network Efficiency

Network Optimization

Machine Learning

Quality of Service (QoS)

Random Forest

Software-Defined Networking (SDN)

\u200eTraffic Classification

Issues

Article Details

Authors: Saroj Singh | Dr. Kamlesh Sharma

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.170-177

Document Type: Article

Publication Date: 7/2025

Document Language: English

Document ID (Org No.): 2001387281

pISSN: 2227-5053

DOI: 10.14419/yxxp2e46

Issues

Authors:

Saroj Singh | Dr. Kamlesh Sharma

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.170-177

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387281

pISSN:

2227-5053

DOI:

10.14419/yxxp2e46