In this lecture we give an introduction to discrete event systems. We start out the course by exploring the limits of what is computable and what is not. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply continuous time markov chains and queueing theory for an understanding of the typical behavior of a system. Then we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queueing. In the last part of the course we introduce methods that allow to formally verify certain properties of Finite Automata and Petri Nets.
In this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. During the fall semester of 2019, the seminar will focus on topics blending networks with machine learning and control theory.
This class will introduce students to advanced, research-level topics in the area of communication networks, both theoretically and practically. Coverage will vary from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Fall Semester of 2019, the class will concentrate on network programmability and network data plane programming.