Open lectures given by professor of Polytechnic University of Milan (Italy) Marco Gribaudoat the Applied Probability and Informatics Department of RUDN University

Professor of the Department of Electronics, Information and Bioengineering of Polytechnic University of Milan (Italy) Marco Gribaudo arrives at the Applied Probability and Informatics Department of RUDN University on May 6 - 12, 2018. His visit is holding in the framework of the RUDN University Competitiveness Enhancement Program «5-100».

Marco Gribaudo graduated from the University of Turin – Italy in 1997. His research interests include performance evaluation and energy optimization of computing systems, cloud infrastructures, Big-Data applications and communication networks. He studies such systems using advanced modeling techniques such as Markovian Agents, mean field approximations, fluid models, multi-formalism models and multi-class queuing networks. Therefore, he supports the development of software tools to define, analyze and evaluate performance models.

Professor Gribaudo is author of 2 books, 33 journal papers and 90 international conference papers – all indexed on the main scientific web resources. Moreover, he presented 6 invited papers (including one keynote speech), 3 tutorials and won two best-paper awards.

Repeatedly, professor Gribaudo gives open lectures for bachelor, master and PhD students of the Applied Probability and Informatics Department of RUDN University. This year, he will present a course of lectures on theoretical methods in the teletraffic theory on the topic of «Performance evaluation of widely distributed systems using Mean Field and Markovian Agents».

Course structure

The course will be composed of 5 seminars of 2 hours each. Seminars will include both theoretical questions and exercises. There will also be practical sessions using computational tools such as Matlab or Octave. Finally, a set of applicative examples will be presented.

Seminar topics

1. Refresh of CTMC for the analysis of queuing networks. Lumping of states to reduce the model state-space. Introduction to counting processes and Mean Field Analysis.
2. Application of Mean Field Analysis to study the performances of real systems.
3. Presentation of the Markovian Agents modelling paradigm and its relation to Mean Field Analysis.
4. Application of Markovian Agents Models to study the performances and reliability of real systems.
5. Introduction to Dynamic Markovian Agents Models, and applications to evaluate mobility and advanced case studies.

Agenda

7 May (Monday): 14:00-17:00 (floor 2, room 114)

10 May (Thursday): 14:00-17:00 (floor 2, room 114)

11 May (Friday): 14:00-17:00 (floor 2, room 114)