Cloud Computing

lingi2145  2017-2018  Louvain-la-Neuve

Cloud Computing
5 credits
30.0 h + 15.0 h
Q2
Teacher(s)
Riviere Etienne;
Language
English
Main themes
  • Architectural principles of cloud computing
  • Scalability of cloud services (storage, computing, ...)
  • Building blocks for cloud services
  • Large scale computations in cloud environments
  • Programming models for cloud services
  • Providing scalable web services from the cloud
Aims

At the end of this learning unit, the student is able to :

1

Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • INFO1.1-3
  • INFO2.2-3, INFO2.5
  • INFO5.2, INFO5.4-5
  • INFO6.1, INFO6.3, INFO6.4

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • SINF1.M1
  • SINF2.2-3, SINF2.5
  • SINF5.2, SINF5.4-5
  • SINF6.1, SINF6.3, SINF6.4

Students having completed this course successfully will be able to

  • explain the goals, benefits and models of cloud computing, providing practical examples
  • describe the main components of cloud computing
  • design and conceive cloud services which operate reliably at scale
  • explain how storage and virtualization are used in the cloud and apply this in practice
  • apply the fundamental principles of multi-tier web applications and services in a cloud environment
  • tackle big data computation problems (e.g., through the Map Reduce computing paradigm)
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
This course focuses on the issues and programming models related to cloud computing environments and distributed data processing technologies: data partitioning, storage schemes, stream processing, and "mostly shared-nothing" parallel algorithms.
Teaching methods
  • Short lectures
  • Scientific readings
  • Quizzes (about readings, labs and lectures)
  • Practical lab sessions
  • Projects
  • Learning by peer-reviewing
     
Evaluation methods
  • Final exam 55%
  • Projects 30%
  • Online quizzes 10%
Some projects and quizzes may involve mandatory peer-review.
For the second examination session, an alternate project will carried out, and more practical aspects will be asked in the exam:
  • Final exam 70%
  • Project 30%
Other information
Background :
  • LINGI1341
  • LSINF1121
Recommended background:
  • Computer networks
  • Have a good understanding of computational complexity

Online resources
https://moodleucl.uclouvain.be/course/view.php?id=9880
Faculty or entity
INFO


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Mathematical Engineering
MAP2M
5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Computer Science
SINF2M
5
Master [120] in data Science: Statistic
DATS2M
5
Master [120] in data Science: Information technology
DATI2M
5