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Description
Data on network utilization and traffic is valuable to optimize network performance. However, collecting direct network measurements or other related raw data from SDN controllers is currently too expensive to be feasible, especially in the case of large-scale high-speed networks (such as 5G). Moreover, the available disk-based or in-memory cache solutions found in SDN controllers are not optimized for the particular data encountered in this context.
The R&D project 5G-data by AppArt, aims to address this issue by investigating the properties of network traffic data available in Software Defined Networks and evaluate/combine various streaming compression techniques to reduce their storage requirements. The key objective is to develop a novel SDN controller that utilizes the most appropriate compression techniques to minimize the storage and bandwidth requirements needed to handle network traffic data, which can then be further analyzed to gain insights on how to optimize the overall network utilization and performance.
5 new jobs will be created by carrying out the project. Additionally, it will enhance the competitiveness of the company in the beneficiary country and increase the revenues.