This course is an introduction to stochastic processes and their application to computer networks. Stochastic processes are described as a sequence of random variables that models the evolution of a system. The course will give a solid background in Markov chains, the most popular analytic tool to model stochastic processes. The course is intended to be as practical as possible, using toy problems to apply all theoretical results presented in the course. About half of the theoretical classes will be dedicated to solve problems. The aim is that through the solution of many insightful examples the students will learn the art of mathematical modeling using Markov chains.
Person in charge
Llorenç Cerdà Alabern (
Technical Competences of each Specialization
Computer networks and distributed systems
CEE2.1 - Capability to understand models, problems and algorithms related to distributed systems, and to design and evaluate algorithms and systems that process the distribution problems and provide distributed services.
CEE2.2 - Capability to understand models, problems and algorithms related to computer networks and to design and evaluate algorithms, protocols and systems that process the complexity of computer communications networks.
CEE2.3 - Capability to understand models, problems and mathematical tools to analyze, design and evaluate computer networks and distributed systems.
Generic Technical Competences
CG1 - Capability to apply the scientific method to study and analyse of phenomena and systems in any area of Computer Science, and in the conception, design and implementation of innovative and original solutions.
CG3 - Capacity for mathematical modeling, calculation and experimental designing in technology and companies engineering centers, particularly in research and innovation in all areas of Computer Science.
CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.
Being able to model a process that evolves over time with a discrete and continuous time Markov chain
Being able to compute the steady state and transient solution of a Markov chain
Being able to model processes that engage the formation of queues
Being able to resolve the basic queues: M/M/1, M/G/1, M/G/1/K
There will be 4 hours per week, dedicated to theoretical classes to explain the theory and solve problems. The students' activities will consist of reading material and solving practical problems that will be proposed at each theoretical unit. The problems will be collected and corrected during the course. There will be research oriented problems to be solved using numerical tools as MATLAB.