Analysis of How Scalable Features in Hadoop/MapReduce by Internet Traffic Management

• Main Authors: Deden Witarsyah Jacob, Deden Witarsyah Jacob, Abd Alkhalec Tharwat, Muhammed.E, Md Fudzee, Mohd Farhan, Ramli, Azizul Azhar, Kasim, Shahreen, Lubis, Muharman
• Format: Article
• Language: English
• Published: Penerbit UTHM 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/11465/1/J15931_b7cfebf54d6cbc1c9fcf9b70b9d156a3.pdf; http://eprints.uthm.edu.my/11465/

Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage.