Improving the Energy Efficiency of Software-Defined Networks through the Prediction of Network Configurations
and Jaime Galán-Jiménez *,†
Round 1
Reviewer 1 Report
In this paper, authors have described a Machine Learning solution based on Logistic Regression to predict energy-efficient network configurations in SDN without the need to execute optimal or heuristic solutions at the SDN controller which require a higher computation time. Experimental results over a realistic network topology show that our solution is able to predict network configurations with a high feasibility (>95%), hence improving the energy savings achieved by a benchmark heuristic based on Genetic Algorithms. Moreover, the time required for computation is reduced by a factor of more than 500,000 times.
The topic is significant in the SDN and networking. The authors have described the solution well.
I have the following recommendations regarding improvements in the paper.
Describe a subsection after introduction i.e., research gap between the previous works (Research Gap)
On page 2, lines 47-49, the importance of central management is described in SDN. Hence, I recommend mentioning some related literature to it. For example, the following works describes the significance of central management and controller in SDN.
1. "Quality of service improvement with optimal software-defined networking controller and control plane clustering." Comput. Mater. Contin 67 (2021): 849-875.
2. “QoS improvement with an optimum controller selection for software-defined networks”. Plos one, 14(5), p.e0217631.
Mention the main contribution points in numbered or bullets form after the introduction section
Include some future directions.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
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Comments for author File: Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have well addressed all my concerns, no further comments.
