Open AccessNode Behavior Classification for Traffic Prediction in Optical Burst Switched Networks using Machine Learning
Author(s) -
Deepali Bhawarthi*,
Dr Girish Chowdhary
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1305.029420
Subject(s) - computer science , adaboost , traffic classification , classifier (uml) , support vector machine , quality of service , artificial intelligence , traffic congestion , network congestion , machine learning , the internet , bandwidth (computing) , computer network , data mining , network packet , engineering , world wide web , transport engineering
Currently due to massive use of internet there is need of huge amount of bandwidth. The utilization of bandwidth can be managed up with optical burst switched networks. These networks cannot provide good QoS due to problems like wavelength contention and congestion problem. Also it is not necessary that contention in a network leads to congestion. It can be due to nodes behavior which affects the flow of traffic from source to destination. Hence there is a need to classify the traffic through the node at correct juncture to avoid congestion. This can be achieved using machine learning techniques. In this paper, support vector machine, AdaBoost classifier and Bagging classifier are evaluated .Experimental work is carried on Optical Burst Switched network dataset using 22 attributes which is available on UCI repository. The results show that bagging classifier performed better with accuracy of 95% in classifying the nodes behavior.