An Improved Classification Model for Wide Area Networks with Low Speed Links
Kate Takyi1, Amandeep Bagga2
1Kate Takyi, Department of Computer Applications, Lovely Professional University, Phagwara, India.
2Amandeep Bagga, Department of Computer Applications, Lovely Professional University, Phagwara, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 165-174 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10420688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The task of network administrators to identify and determine the type of traffic traversing through the network is very critical with the rapid growth of new traffic each day. Considering wide area networks with limited resources in terms of low speed links, quantified amount of packets are likely to be lost which lowers the quality of service. The classification procedure in such scenarios can also be affected due to the limited features extracted from the various fragments of packets that will successfully get to the destination node or server. We propose a hybrid cluster and label algorithm, which is able to classify application traffic or packets, utilizing restricted traffic features, few packets and at the same time maintains a low complexity and good classification accuracy. A wide area network exposed to extreme packet loss scenario is designed and implemented using OMNET ++ simulation to generate a dataset. The proposed model is built and tested in MATLAB simulation environment. Evaluation results shows that our proposed semi-supervised algorithm achieves an accuracy of 92.4% in classification with lower error rates of 7.4% and 2.9839 seconds processing time.
Keywords: Clustering Techniques, K-Medoids, Packet Loss, Support Vector Machines
Scope of the Article: Clustering