Missing Internet Traffic Reconstruction using Compressive Sampling

Indrarini Dyah Irawati, Andriyan Bayu Suksmono, Ian Ian Yosef Matheus Edward

Abstract


Missing traffic is a commonly problem in large-scale network. Because the traffic information is needed by network engineering task for network monitoring, there are several methods that recover the missing problem. In this paper, we proposed missing internet traffic reconstruction based on compressive sampling. The main contributions of this study are as follows: (i) explore the influence of the six missing patterns on the performance of the traffic matrix reconstruction algorithm; (ii) trace the link sensitivity; and (iii) detect the time sensitivity of the network. Using Abilene data, the simulation results show that compressive sampling can perform internet traffic monitoring such as reconstruction from missing traffic, finding link sensitivity, and detecting time sensitivity.

 


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International Journal of Communication Networks and Information Security (IJCNIS)          ISSN: 2076-0930 (Print)           ISSN: 2073-607X (Online)