%0 Journal Article %1 noauthororeditor %A Jain, Ankur %A Gupta, Arohi %A Gupta, Ashutosh %A Gedia, Dewang %A Pérez, Leidy %A Levi, %A Perigo, %A Gandotra, Rahil %A and Sanjay Murthy, %D 2019 %J International Journal of Next-Generation Networks (IJNGN) %K Analysis Ansibl Big Collectd Data Jupyter Kafka Logstash Network Networking OpenFlow PNDA Python Ryu SNMP Software-defined Trend %N 01 %P 01-15 %R 10.5121/ijngn.2019.11101 %T TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL NETWORKS %U https://aircconline.com/ijngn/V11N1/11119ijngn01.pdf %V 11 %X Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.

@article{noauthororeditor, abstract = {Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.}, added-at = {2022-03-10T12:40:02.000+0100}, author = {Jain, Ankur and Gupta, Arohi and Gupta, Ashutosh and Gedia, Dewang and Pérez, Leidy and Levi and Perigo and Gandotra, Rahil and and Sanjay Murthy}, biburl = {https://www.bibsonomy.org/bibtex/2a1151b2793299e57b1c519d33489ecd3/josephjonse}, doi = {10.5121/ijngn.2019.11101}, interhash = {c55b18c018f817012fc9442f5c940783}, intrahash = {a1151b2793299e57b1c519d33489ecd3}, issn = {ISSN : 0975-7023 Online); 0975-7252 ( Print )}, journal = { International Journal of Next-Generation Networks (IJNGN)}, keywords = {Analysis Ansibl Big Collectd Data Jupyter Kafka Logstash Network Networking OpenFlow PNDA Python Ryu SNMP Software-defined Trend}, language = {English}, month = {March}, number = 01, pages = {01-15}, timestamp = {2022-03-10T12:40:02.000+0100}, title = {TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL NETWORKS}, url = {https://aircconline.com/ijngn/V11N1/11119ijngn01.pdf}, volume = 11, year = 2019 }