Data centric redundancy elimination for network data traffic. (5th May 2023) - Narayanan, Sandhya; Samuel, Philip; Chacko, Mariamma
Network traffic occurring in the internet is a challenging issue due to the increase in internet users. Nowadays, internet traffic increases exponentially every month. Communication capability between the networks becomes difficult because of this heavy traffic. We propose hashing-based network resilient distribution (HBNRD) method to detect and eliminate duplicate data chunks in the packets of network layer using big data processing framework. HBNRD method helps to detect the similar files transferred through the internet and removes redundant data chunks. This results in fast communication and the proposed model attains fault tolerance because of resilient distributed approach. This data centric model can dynamically allocate the resources and can detect the data chunk repetition occurring during computation. Use of Center for Applied Internet Data Analysis (CAIDA) dataset shows that HBNRD improves the network performance by reducing the internet traffic redundancy by 60%. The data centric network traffic redundancy eliminated model is fast, scalable and resilient.