Course
Cloud Computing Technologies (DAT515)
Facts
Course code DAT515
Credits (ECTS) 5
Semester tution start Autumn
Language of instruction English
Number of semesters 1
Exam semester Autumn
Time table View course schedule
Literature Search for literature in Leganto
Introduction
Cloud computing enables the dynamic provisioning of ubiquitous, on-demand computing resources, storage space, software applications, and services over the Internet with little to no explicit interaction with the service provider.
Content
Learning outcome
Knowledge
- Characterize and compare typical service models like IaaS, PaaS, and SaaS.
- Understand elements of Cloud infrastructures.
- Understand and compare the most common commercial Cloud offerings.
- Compute abstractions, including virtual machines, containers, and serverless computing.
- Storage abstractions with varying consistency requirements.
- Resource management, including storage and container management.
- Ethical, environmental, and legal implications of Cloud technologies, e.g., United Nation's Sustainable Development Goals, GDPR, and MLAT/CLOUD Act.
Skills
- Be able to design a Cloud-based solution based on a given specification.
- Be able to deploy a Cloud-based solution optimized to available resources.
- Be capable of implementing applications that utilize cloud APIs on the application layer, e.g., GitHub, Discord, and the storage/compute layer.
- Be able to analyze the security risks of a specific Cloud-based deployment.
- Be capable of implementing secret management for Cloud applications using, e.g., Passkey, credentials, passwords, or tokens.
Required prerequisite knowledge
Recommended prerequisites
Exam
Report
Weight 1/1
Marks Letter grades
The course assessment is based on a group project. The grade is determined by the submitted code and project documentation.
The project is to implement a cloud application and document it according to the specified requirements. The documentation must include complete instructions to run the application, including the installation of dependencies, and exercise the execution of the cloud application with test scenarios. The installation process should be automated as much as possible. The documentation must also include a YouTube video showcasing the group’s project (maximum 10 minutes).
The project is done in groups and all participants in the group get the same grade. All participants must contribute equally to the project’s execution and must document their contribution. The project’s grade is awarded based on the function and quality of the developed cloud application, including the code, tests, and the documentation. It is not possible to retake the project next semester. Students that wish to take the project part again, must do this the next year the course is given.
The use of AI tools is allowed and recommended in this course. However, it is the students responsibility to verify that the information and code produced with the help of such tools is accurate and correct. Any inaccuracies or misleading content in the submitted documentation may negatively affect the grade.
Coursework requirements
Required programming and system administration tasks. To get approval, you need to submit your solutions to our system for automated evaluation. Approval also requires an in-lab presentation of your solutions after submission. All mandatory assignments must be completed to be eligible to submit the final report and pass the course.
Mandatory lab assignments must be completed at the specified times and in the assigned groups. Absence due to illness or other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon.
Failure to complete or have the assignments approved on time will result in failing the course. If a student does not pass the mandatory assignments, the project report, or fails to attend the project presentation, they must redo all assignments and the written report the next time the course is offered.
Method of work
The course lectures will run in the first half of the semester. 4 hours of lectures and 4 hours of guided laboratory exercises per week. Laboratory exercises and project will require additional non-guided work effort. The total weekly workload, including self-study and development work, is expected to be 15 hours for the course's duration.
The work is carried out in groups of 2-4 students.