A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources
Kate Takyi1, Amandeep Bagga2, Pooja Gupta3

1Kate Takyi, Department of Computer Applications, Lovely Professional University, Phagwara, India.
2Amandeep Bagga, Department of Computer Applications, Lovely Professional University, Phagwara, India.
3Pooja Gupta, Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India.
Manuscript received on 26 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 970-981 | Volume-8 Issue-11, September 2019. | Retrieval Number: H7185068819/2019©BEIESP | DOI: 10.35940/ijitee.H7185.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 to the rapid growth of new traffic each day. As the requirements of networks change over time, the situation of the network not able to meet some requirements is likely to occur. In a wide area network with a limited resource such as the low speed of links, frequent fragmentation of packets leading to extreme packet loss and costs is prominent resulting in the poor quality of service. As a result quantified amount of traffic flows can be classified at a time with limited features lowering the effectiveness of traffic classification. To improve upon the classification in such scenarios, we propose a hybrid semi-supervised clustering that is able to classify packet flows with restricted features and a small amount of packets while maintaining high accuracy in classification. We implement the above scenario in simulation and classify the limited flows obtained with our proposed algorithm. Evaluation results show that our proposed algorithm implemented into a classifier has good accuracy and precision values, with low processing time and error rates. The proposed strategy will enable network administrators during times of network resource depletion or upgrades provide and ensure the best quality of services and identify unwanted or malicious traffic.
Keywords: Clustering Algorithms, Quality of Service, Machine Learning, K-Medoids, Packet loss
Scope of the Article: