Category	Details
Id	ITU-ML5G-PS-SODGANS
Title	Synthetic Observability Data Generation using GANs
Description	Observability data can be any of the following: Telco-Cloud Infrastructure Metrics (servers) H/W Level OS Level Virtualization Level Metrics/Statistics from Physical Network Elements. Application (virtualized Network functions) Metrics System logs (Servers, Applications, etc.) Metrics/Statistics from centralized orchestration systems (VIM, SDN-Controller, VNFM, NFVO, etc.) Metrics/Statistics from other control-plane services Availability of these data for AI/ML researchers, who are not part of the Telco, is very difficult. To solve this availability issue, one approach is to generate synthetic observability data. In this project, we propose to generate this synthetic observability data using GANs. For this first round only Telco-Cloud Infrastructure Metrics will be considered.
Challenge Track/Theme	???
Evaluation criteria	Submission of the GANs implementation as Python (or any other language) program. Least error from the descriminatordiscriminator.
Data source	Real-World observability data (Telco-Cloud Infrastructure Metrics) will be provided
Resources	Computing resources can be provided to those who do not have access to one.
Any controls or restrictions	Anything around data, use of the models, ...
Specification/Paper reference
Contact	Sridhar K. N. Rao sridharkn@u.nus.edu

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