Decentralized Systems

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Credits
6
Types
Specialization complementary (Computer Networks and Distributed Systems)
Requirements
This subject has not requirements, but it has got previous capacities
Department
AC
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).

Teachers

Person in charge

  • Felix Freitag ( )
  • Leandro Navarro Moldes ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
7

Competences

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.

Specific

  • 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

Generic

  • CG5 - Capability to apply innovative solutions and make progress in the knowledge to exploit the new paradigms of computing, particularly in distributed environments.

Transversal Competences

Reasoning

  • 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.

Objectives

  1. Review papers
    Related competences: CEE2.2, CEE2.1, CEC3, CTR6, CG5,
    Subcompetences:
    • Participation in activities
    • Paper reviews and assessment
    • Work on specific topic

Contents

  1. Fundamental concepts
    Peer-to-peer and overlay networks
  2. Routing in overlay networks
    Routing in unstructured and structured overlay networks
  3. Techniques and models
    Publish/subscribe, group communication, self-properties, incentives, management, resource allocation, security and anonymity, characterization and evaluation.
  4. Applications
    Content and media distribution, storage, file sharing, communication, computing, social networks

Activities

Activity Evaluation act


Course presentation



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
1h

Fundamental concepts in peer-to-peer and overlay networks


Objectives: 1
Theory
10h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Routing in unstructured and structured overlay networks



Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Techniques and models



Theory
10h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Applications



Theory
8h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Course work proposal



Week: 8
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Discussion leader



Week: 6
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Paper review work



Week: 11
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
20h

Q&A research



Week: 14
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Presentation of course work



Week: 14
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
34h

Proposal course work



Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Discussion leader



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Paper review work



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Q&A research



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Presentation final course work



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

Theory and participatory classes, readings of research papers, presentation of topics by students, development of a course work.

Evaluation methodology

The evaluation of the course is based on the participation of students in class activities, the students' review and assessment of reports/papers and the development of a course work on specific topics.

NF = 0,3 * PR + 0,2 * PAR + 0,5 * DT
where:

NF = Final mark of the course
PR = Paper reviews and assessment
PAR = Participation in activities
DT = Work on specific topic

Bibliography

Basic:

  • The course will not rely on any basic bibliography, but on a set of research papers that address topics of the different sections of the program of the course. - ,

Previous capacities

Computer networks.