Cloud Computing Technologies (DAT515)

The Cloud computing model 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.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

DAT515

Version

1

Credits (ECTS)

5

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

Content

This course introduces established cloud computing service models, technology platforms, and applications. You will learn about the implementation and administration of Cloud computing systems. You will securely deploy, manage, and scale your applications using an established Cloud infrastructure, virtual machines or containers, and serverless computing. Your applications will interact with the hosting infrastructure via APIs.

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

None

Recommended prerequisites

DAT230 Communication Technology I, DAT250 Information and Software Security, DAT320 Operating Systems and Systems Programming

Exam

Form of assessment Weight Duration Marks Aid
Report 1/1 Passed / Not Passed

Mandatory programming and system administration assignments. Students must complete all assignments to be eligible to submit the final report. Approval of assignments requires in-lab presentations of your solutions. The course grade will be determined based on the submitted code and the project report document. Both components must be completed before the final grade is assigned. If a student fails to pass all the mandatory assignments or the report, then the student must redo all assignments and the written report when the subject is taught again.

Coursework requirements

Mandatory Assignments, Presentation

Mandatory work 1: Laboratory work

Approval may require submitting your solution to our system for automated evaluation, followed by in-lab approval. All assignments must be approved to 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 the assignments on time or not having them approved will result in failing the course.

Mandatory work 2: Oral presentation of submitted program code and report. All group members must participate in the oral presentation.

Course teacher(s)

Course coordinator:

Hein Meling

Laboratory Engineer:

Jayachander Surbiryala

Head of Department:

Tom Ryen

Study Program Director:

Tomasz Wiktorski

Method of work

The course will run in the first half of the semester.

4 hours of lectures and 4 hours of guided laboratory exercises. Laboratory exercises 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.

Open for

Data Science - Master of Science Degree Programme Datateknologi - master i teknologi/siv.ing.
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

The syllabus can be found in Leganto