Specialization complementary (Computer Networks and Distributed Systems)
This subject has not requirements
The goal of this course is introducing the student in the research topics related to descentralized and scalable systems. The program will consist in overlay networks, topological properties of the Internet, network coordinates,decentralization topics and systems (optimistic replication, publish-subscribe, content distribution, volunteer computing, sensor networks), scale in systems properties, issues in large-scale systems (virtualization, service orientation and composition, availability, locality, performance and adaptation), system models (game-theoretic, economic, evolutionary, control, complexity), architectural models (multi-tier, cluster, grid, cloud, SaaS), middleware and applications (Grid/Cloud, coordination, computing, storage, web, content distribution, Internet-scale systems or services).
Person in charge
Felix Freitag (
Leandro Navarro Moldes (
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.
CEC3 - Ability to apply innovative solutions and make progress in the knowledge that exploit the new paradigms of Informatics, particularly in distributed environments.
Generic Technical Competences
CG5 - Capability to apply innovative solutions and make progress in the knowledge to exploit the new paradigms of computing, particularly in distributed environments.
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.