Cloud Computing

Teacher(s)

Riviere Etienne;

Language

English
> French-friendly

Prerequisites

You would already have passed LINGI2172 Databases

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

Learning outcomes

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

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, INFO6.4, INFO6.5 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, SINF6.4, SINF6.5 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).

Content

This course focuses on the use and understanding of modern cloud computing technologies. It covers the systems aspects of dematerialized computing, including virtualization, storage, and fault tolerance; as well as software engineering aspects such as the construction of elastically scalable service-oriented applications backend. The course also covers data management and processing in the cloud, and its integration into cloud applications, as well as an introduction to advanced topics such as cloud security and decentralized trust. The course also discusses the environmental impact of the cloud and resource management techniques allowing to limit it. Concepts and tools covered in class are applied in a project where students build from the ground up a cloud-native backend for a representative application.

Teaching methods

• Lectures
• Scientific readings or/and videos from the industry
• Quizzes (about readings, labs and lectures)
• Practical lab sessions (tutorials)
• Project
   

Evaluation methods

The final grade is computed as follows for the first session (January): 

• Project 45%
• Final exam 45%
• Participation in online quiz and to the peer review of other students' work 10%

It will not be possible to redo the project or the quizzes for the second session. However, the scale for the second session (September) is changed to:

• Project 45%
• Final exam 55%

Continuous evaluation initially proposed with formative evaluation only could be graded and account for all or a part of the grade devoted to the final exam if dictated by circumstances.
The professor may request that a student take an additional oral exam in addition to the final exam and/or the project in cases including, but not limited to, technical issues or suspicion of irregularities.
The project is evaluated and graded for each of its two parts. Any violation of deontological obligations (including but not limited to plagiarism, collaboration with students outside of the project group or with third parties, voluntary or involuntary sharding of code, e.g., via a public GitHub repository, etc.) will result in a grade of 0 for the entirety of the project, and students will be denounced to the relevant authorities.
The exam may use all or a subset of the following evaluation modalities. The respective proportion of points for each part is announced at the beginning of the exam:

• open questions on the course content
• open problems requiring an application of skills and knowledge acquired during the course
• multiple-choice and multiple-answer questions under the principle of the "standard-setting". An incorrect answer to one of the questions cannot lead to a negative grade, and the exam part as a whole cannot grant negative points. However, a minimum threshold (announced in the exam) of correct answers is necessary before effectively acquiring points for this exam part.

Rules regarding the use of artificial intelligence (AI) for continuous assessment activities
The use of AI is prohibited for answering bi-weekly quizzes or providing feedback during the peer evaluation phase. This activity only has pedagogical value if students express their understanding of the course in their own words. Answers clearly produced by generative AIs (ChatGPT and similar tools) may lead to an oral examination of the student concerned on their mastery of the answer, and if this mastery is lacking, to an overall grade of 0 for the part devoted to quizzes and their evaluation (10% of the grade for the January session).
The use of AI for the project is not recommended, as completing the project manually is the most effective way to acquire the skills targeted by the course. If its use is authorized (whether to generate code or documentation), it is subject to the following rules:

• Students must take full responsibility for their work and be able to explain orally all the code and deliverables (documentation, deployment scripts, etc.) submitted as part of the project.
  
• The use of AI must be precisely documented in the project documentation, in a dedicated section indicating which AIs were used and for which part. Submitting code or documentation generated partially or entirely by AI without documenting this use will be considered plagiarism. Students who did not use AI must also indicate this in the section.
  
• Use considered abusive of AI, undermining the acquisition of the knowledge targeted by the project, may be considered an irregularity under Section 7, Articles 107 and following of the General Regulations for Studies and Exams (RGEE), with all the consequences that entail, as provided in Articles 111 and following. In the event of suspected abusive use of AI in the submitted project or an incomplete or inaccurate report of AI use, the course instructor may summon the student concerned for an additional oral consultation and take the necessary measures in consultation with the EPL jury chair.
  

Other information

Required background:

• LINFO1252

Recommended background:

• LINFO1341
• LINFO1121

It is, in general, recommended to have good notions in networking, operating systems, and databases. The professor can advise supplementary reading to catch up on these topics upon request.
 

Online resources

https://moodleucl.uclouvain.be/course/view.php?id=14866

Faculty or entity

> INFO