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Telecommunication Networks Group

Theses at Politecnico

Analysis and optimization of performance of the Virtual Classroom platform for e-learning

keywords E-LEARNING, NETWORK MEASUREMENTS, NETWORK PERFORMANCE

Reference persons MARCO MELLIA, MARTINO TREVISAN

Research Groups CCNE - COMMUNICATIONS AND COMPUTER NETWORKS ENGINEERING, ICT4SS - ICT FOR SMART SOCIETIES, SmartData@PoliTO, Telecommunication Networks Group

Thesis type EXPERIMENTAL / DEVELOPMENT

Description With the transition to DAD and online teaching, for more than a year we have been projected on e-learning and online collaboration platforms that have allowed us to continue teaching, exams and work even remotely.
More than a year after its activation based on the BBB / Virtual Classroom platform, the Politecnico was one of the forerunners in using an on-premises solution instead of relying on cloud providers. This allowed us to gain a unique experience, and to collect data and statistics in abundance. An example of possible analysis is https://www.sciencedirect.com/science/article/abs/pii/S1389128620306046
This thesis aims to analyze the data collected by servers, network monitoring systems, clients, etc., to identify problems and point out possible improvement of the platform itself.
Thanks to a collaboration with TIM, it will also be possible to integrate the observed data with information relating to the network and configuration of students and users who have TIM as a provider.
In a first phase, the data in possession will be processed in order to characterize the service and highlight any problems. In a second phase, possible improvements on the network or service platform will be defined.
The thesis will be held in collaboration with the SmartData @ PoliTO Interdepartmental Center and TIM.

See also  https://www.sciencedirect.com/science/article/abs/pii/S1389128620306046

Required skills Excellent knowledge of the functioning of TCP/IP networks and streaming protocols based on RTP
Excellent familiarity with data analysis with Python platforms such as Pandas
Average higher than 27/30


Deadline 31/03/2022      PROPONI LA TUA CANDIDATURA




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