Teacher(s)
Language
English
> French-friendly
> 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 : | |
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:
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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):
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:
- Project 45%
- Final exam 45%
- Participation in online quiz and to the peer review of other students' work 10%
- Project 45%
- Final exam 55%
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.
Other information
Required background:
- LINFO1252
- LINFO1341
- LINFO1121
Online resources
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in Mathematical Engineering
Master [120] in Data Science Engineering
Master [120] in Data Science: Information Technology