PhD program in Electrical, Electronics and Communications Engineering
PhD Student: Dena Markudova
Context of the research activity
The internet and in particular the web has embraced widespread encryption of contents, with HTTPS carrying more than 90% of traffic. This has hampered the ability of in-network devices to classify traffic, and assign a proper QoS/QoE class.
Here we focus explicitly on the design of novel techniques to re-obtain visibility on application traffic, empowering novel machine learning (ML) algorithms to automatically classify traffic stream to applications and to design management policies to improve real-time application QoE. We explicitly target multiparty online collaboration applications, from online conference, to multiplayer gaming, from high quality video streaming to potentially connected car services. All these services require strict QoS, where fine grained classification is needed to distinguish top priority flows (e.g., audio) from possibly less sensitive data exchange (e.g., video).
The research activity fits in the SmartData@PoliTo interdepartmental centre, that brings together competences from different fields, ranging from modelling to computer programming, from communications to statistics. The candidate will join this interdisciplinary team of experts and collaborate with them.
The candidate should tackle the problem by collecting, storing and processing large amount of data using a big data framework. The candidate will leverage these solutions, designing and engineering novel machine learning techniques to tackle the fine-grained traffic classification problem.
The candidate will design a complete solution, from data collection, feature engineering, feature selection, model selection, model training and testing.
Next, the candidate should revisit and propose new QoS mechanisms to address the specific needs of the applications and the network.
Skills and competencies for the development of the activity
The candidate must have excellent knowledge of computer networks, machine learning techniques, data analysis methodologies and statistics. The candidate should have excellent programming skills, with also knowledge of Big Data platforms, such as Hadoop, Spark, Hive, Flume.
Further information about the PhD program at Politecnico can be found here
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