Cloud Services (5 cr)

Evaluation scale

H-5

Content scheduling

The course content is divided into four learning objectives(CLOs):

CLO1 Analyze classic data centers and cloud data center solutions.

Introduction to Cloud Computing
1.1 Understand the limitations of traditional computing and evolution of cloud computing
1.2 Understand the concepts of Cluster, Grid and Cloud Computing, its benefits and challenges

Cloud Computing Models and Services
1.3 Explore the standard cloud model, cloud deployment and service delivery models
1.4 Understand service abstraction

Resource Virtualization and Pooling
1.5 Implement physical computing resources virtualization
1.6 Implement machine, server level and operating system virtualization
1.7 Understand resource pooling, sharing and resource provisioning

CLO2 Design a cloud data center based on specific technical requirements.

Resource Virtualization and Pooling
2.1 Implement physical computing resources virtualization
2.2 Implement machine, server level and operating system virtualization

Scaling and Capacity Planning
2.3 Understand the foundation of cloud scaling
2.4 Explore scaling strategies and implement scalable applications
2.5 Explore approaches for capacity planning

Load Balancing
2.6 Explore the goals and categories of load balancing. Explore parameters for consideration.

File System and Storage
2.7 Understand the need for high performance processing and Big Data
2.8 Explore storage deployment models and differentiate various storage types

CLO3 Discuss the need for security, reliability and legal compliance of a cloud data center.
Database Technologies
3.1 Explore database models
3.2 Implement relational and non-relational database as a service

Cloud Computing Security
3.3 Understand the threats to cloud security
3.5 Explore and develop a cloud security model
3.6 Understand Trusted Cloud Computing

Privacy and Compliance
3.7 Explore key privacy concerns in the cloud
3.8 Differentiate security vs. privacy
3.9 Develop a privacy policy

CLO4 Design strategies for the implementation of effective cloud solutions to support business requirements.

Content Delivery Model
4.1 Understand and explore content delivery network models in the cloud

Portability and Interoperability
4.2 Explore portability and interoperability scenarios
4.3 Understand machine imaging
4.4 Differentiate virtual machine and virtual appliance

Cloud Management
4.5 Understand cloud service life cycle
4.6 Understand asset management in the cloud
4.7 Explore cloud service management
4.8 Develop disaster recovery strategies

SELF PACED / FLIP CLASSROOM
In addition to the above theoretical content the students will learn and practice the cloud concepts in AWS academy. The AWS academy online course covers the following modules.

Introduction
Module 1 - Cloud Concepts Overview
Module 2 - Cloud Economics and Billing
Module 3 - AWS Global Infrastructure Overview
Module 4 - AWS Cloud Security
Module 5 - Networking and Content Delivery
Module 6 - Compute
Module 7 - Storage
Module 8 - Databases
Module 9 - Cloud Architecture
Module 10 - Auto Scaling and Monitoring

Objective

After completing the course, the student can:
- Describe what Cloud Services are, how they affect business and which new opportunities it may enable.
- Describe use cases and benefits of cloud services,
- Describe SAAS, PAAS, IAAS.
- Develop a solution that utilizes cloud services.

Content

Introduction to cloud services
Software as a service (SAAS)
Platform as a service (PAAS)
Infrastructure as a service (IAAS)
Virtual machines and containers
Security of cloud services
Project work

Materials

Task-specific material to be announced separately in Its Learning and in AWS academy.

Teaching methods

- Weekly face-to-face meetings with lecture teaching and small group work
- Learning by doing and experimenting (exercise tasks, project work, information search)
- Small group work and peer learning
- Self-study material
- Teacher guidance and examples

Exam schedules

No exam, and retake not possible after evaluation grade is published.

Pedagogic approaches and sustainable development

The Cloud Services course applies pedagogic methods like flipped learning, hands-on labs, and problem-based projects using platforms such as AWS Academy. These methods help students build real-world skills in deploying, managing, and securing scalable cloud solutions. Peer collaboration and continuous feedback support active engagement and reflection.

The course aligns with sustainable development goals by addressing energy-efficient computing, digital access and inclusion, and industry innovation. Students explore how cloud solutions can reduce hardware waste, optimize resource use, and support remote, low-energy infrastructure. Projects may include deploying serverless applications or calculating the carbon footprint of various cloud architectures.

This integrated approach prepares students to design sustainable, scalable, and socially responsible cloud solutions.

Completion alternatives

Self-paced learning

Student workload

Contact hours
- Course introduction: 3 hours
- 12 times 2h theory: 12 x 2h = 24 hours
- 12 times 1h AWS Support: 12 x 1h = 12 hours
- 10 times AWS Academy self paced sessions: 10 x 2h = 20 hours

Home work:
- Working with assignments: approximately 80 hours

Total: approximately 135 hours

Evaluation methods and criteria

Personal assignments: 50 points
AWS Academy Course labs: 30 points
Project: 20 points

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.
Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

The grading scale (points -> grade):

50 points -> 1
60 points -> 2
70 points -> 3
80 points -> 4
90 points -> 5

Failed (0)

Fail < 50 points

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Further information

Course material and assignments in Its Learning and AWS academy.

USE OF ARTIFICIAL INTELLIGENCE REPORTED
Allowed, can be used, must be reported. Artificial intelligence can be used in the creation of outputs, but the student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the assessment.