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COMP30520

Academic Year 2025/2026

Cloud Computing (UG) (COMP30520)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Dimitris Chatzopoulos
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Nowadays Cloud Computing is everywhere. Cloud Computing (CC) is not a revolution of Information technology (IT), but It is one of the key evolution steps of IT. It provides computing as a utility, which has recently emerged as a commercial reality. The main characteristics of CC are 1) the illusion of infinite computing and storage resources, 2) the ability to pay-as-you-use, and 3) the elimination of an up-front commitment by Cloud users. In other words, CC is a style of computing in which can be scaled dynamically and virtualised resources are provided as a service over the Network. The key idea behind this course is to provide fundamental CC topics taking into account both technology and business considerations. The course is divided into a series of lectures, each of which is accompanied by one or more hands-on exercises. Among the topics that will be covered in this course are:
• Fundamental CC terminology and concepts
• CC definition an its specific characteristics
• Benefits, Challenges and Risks of CC platforms and Services
• Roles of CC administrator and owners
• SaaS, PaaS, and IaaS delivery models and their combinations
• Various Public, Private, and hybrid CC environments
• Business Cost models and Service Level Agreements for CC
• Case Studies: Google Cloud, Microsoft Cloud, and Amazon Cloud

About this Module

Learning Outcomes:

• Explain Cloud Computing and identify its capabilities.
• Analyse computer systems in terms of their Elasticity, Resiliency, On-Demand Services and Measured Usage.
• Compare Cloud Services, Cloud Platforms, and Cloud Infrastructures.
• Revise how the Cloud integrates technologies like Parallel or Distributed computing, Microservices, and Software Pipelines.
• Interpret the impact of economies of scale on cloud computing and identify the main security problems that affect this technology.
• Design and compare Cloud Computing applications based on monolithic designs and microservices.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

36
Autonomous Student Learning

65
Total

125

Approaches to Teaching and Learning:
The module will be delivered based on the following components:
- 2 lectures per week for 12 weeks
- 1 practical/tutorial session of 2 hours for 12 weeks. During these sessions, we will experiment with some cloud computing software tools and services and we will study some concepts in detail.

Please note that in cases where students are permitted to use generative AI tools in assignments, this will be indicated in the assignment specification.

Requirements, Exclusions and Recommendations
Learning Recommendations:

• Operating Systems
• Computer Networks
• Big Data Programming


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Two projects that assess the technical parts of the module Week 6, Week 12 Alternative linear conversion grade scale 40% No
20
 Yes
Exam (In-person): Closed book exam that assesses everything discussed during the semester. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
60
 No
Exam (In-person): In-class test that assess the theoretical parts of the module Week 10 Alternative linear conversion grade scale 40% No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

feedback will given to students after each practical/tutorial submission.

Name Role
Theodoros Aslanidis Tutor
John Byabazaire Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - 2, 3, 4, 6, 7, 9, 10, 11, 12 Mon 15:00 - 16:50
Autumn Exam Mid-term (ALU) Offering 1 Week(s) - 5 Mon 15:00 - 16:50
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12 Tues 09:00 - 10:50
Autumn Exam Mid-term (ALU) Offering 1 Week(s) - 10 Tues 09:00 - 10:50
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