Publication:
MER-SDN

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorAssefa, Beakal Gizachew
dc.contributor.kuauthorÖzkasap, Öznur
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T22:50:18Z
dc.date.issued2018
dc.description.abstractSoftware Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of information is generated in the controller of an SDN based networks. Machine learning gives the ability to computers to progressively learn from data without having to write specific instructions. In this work, we propose MER-SDN: a machine learning framework for traffic aware energy efficient routing in SDN. Feature extraction, training, and testing are the three main stages of the learning machine. Experiments are conducted on Mininet and POX controller using real-world network topology and dynamic traffic traces from SNDlib. Results show that our approach achieves more than 65% feature size reduction, more than 70% accuracy in parameter prediction of an energy efficient heuristics algorithm, also our prediction refine heuristics converges the predicted value to the optimal parameters values with up to 25X speedup as compared to the brute force method.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-1
dc.identifier.isbn978-1-5386-7518-2
dc.identifier.scopus2-s2.0-85056889397
dc.identifier.urihttps://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-1
dc.identifier.urihttps://hdl.handle.net/20.500.14288/6652
dc.identifier.wos450146600147
dc.keywordsSoftware-defined networking
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2018 16th IEEE Int Conf On Dependable, Autonom And Secure Comp, 16th IEEE Int Conf On Pervas Intelligence And Comp, 4th IEEE Int Conf On Big Data Intelligence And Comp, 3rd IEEE Cyber Sci And Technol Congress (Dasc/Picom/Datacom/Cyberscitech)
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectTheory methods
dc.subjectEngineering
dc.subjectElectrical electronic engineering
dc.titleMER-SDN
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorAssefa, Beakal Gizachew
local.contributor.kuauthorÖzkasap, Öznur
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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