Part A: Course Overview

Course Title: Cloud Computing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2626

City Campus

Undergraduate

140H Computer Science & Information Technology

Face-to-Face

 Summer2016

COSC2626

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 1 2020,
Sem 1 2021

COSC2626

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Sem 1 2022,
Sem 1 2023,
Sem 1 2024,
Sem 1 2025

COSC2638

RMIT University Vietnam

Undergraduate

171H School of Science

Face-to-Face

 Viet3 2018,
Viet3 2019,
Viet1 2020,
Viet2 2021

COSC2638

RMIT University Vietnam

Undergraduate

175H Computing Technologies

Face-to-Face

 Viet2 2022,
Viet1 2023,
Viet1 2025

COSC2639

Open Learning Australia

Non Award

171H School of Science

Distance / Correspondence

 OUASP2UG21

COSC2639

Open Learning Australia

Non Award

175H Computing Technologies

Distance / Correspondence

 OUASP1UG24

COSC2724

RMIT Vietnam Hanoi Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Viet1 2025

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2626

City Campus

Undergraduate

171H School of Science

Face-to-Face

 UGRDFlex17 (ZZZZ)

COSC2626

City Campus

Undergraduate

171H School of Science

Face-to-Face

 UGRDFlex18 (ZZZZ)

COSC2626

City Campus

Undergraduate

171H School of Science

Face-to-Face

 UGRDFx2019 (ZZZZ)

COSC2626

City Campus

Undergraduate

171H School of Science

Face-to-Face

 UGRDFlex21 (All)

COSC2697

OUA CSP

Undergraduate

171H School of Science

Internet

 OUACSP2021 (All)

Course Coordinator: Dr. Qiang Fu

Course Coordinator Phone: .

Course Coordinator Email: qiang.fu@rmit.edu.au

Course Coordinator Location: 14.11.32

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite Courses
Successful completion of: 

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course. 

For information go to RMIT Course Requisites webpage. 

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.

 

Assumed knowledge
It is assumed that you have:

  •  basic Python, JavaScript and PHP programming skills
  •  basic understanding of Data Communications and Networking Technologies
  •  basic understanding of College level (or first year undergrad type) Mathematics
  •  ability to write technical reports


Course Description

Cloud Computing is a large-scale distributed computing paradigm which has become a driving force for information technology over the past several years. The exponential growth data size in scientific instrumentation/simulation and social media has triggered the wider use of cloud computing services.

This course covers topics and technologies related to Cloud Computing and their practical implementations. You should explore different architectural models of cloud computing, the concepts of virtualisation and cloud orchestration. You should gain hands-on experience with various features of popular cloud platforms such as Amazon Web Service throughout the lectorials, tutorials, and laboratory sessions. Advanced cloud programming paradigms such as Hadoop’s MapReduce is also included in the course. You should also learn the concept of modern Big Data analysis on cloud platforms using various data mining tools and techniques. The lab sessions cover cloud application development and deployment, use of cloud storage, creation and configuration of virtual machines and data analysis on cloud using data mining tools. Different application scenarios from popular domains that leverage the cloud technologies such as remote healthcare and social networks will be explained. The theoretical knowledge, practical sessions and assignments aim to help you to build your skills to develop large-scale industry standard applications using cloud platforms and tools.

This course focuses on learning emerging issues related to Cloud computing technology. 


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

Major - Advanced Computer Science

  • BP094P23 - Bachelor of Computer Science
  • BP347 - Bachelor of Computer Science (Professional)

PLO 1    Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.
PLO 2    Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
PLO 3    Cognitive and Technical Skill - Critically analyse and evaluate user requirements and design systems employing software development tools, techniques and emerging technologies.

BH101CS - Bachelor of Science (Dean's Scholar, Computer Science) (Honours)

PLO 1    Enabling Knowledge
PLO 2    Critical Analysis
PLO 3    Problem Solving
PLO 4    Communication
PLO 5    Teamwork

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you should be able to:

  1. Explain the key concepts and principles of cloud computing and the possible applications of the technology
  2. Develop and deploy highly scalable cloud applications that are resilient, elastic and cost-efficient
  3. Identify the big data analysis techniques to store and analyse data in cloud computing
  4. Compare and evaluate the key trade-offs between multiple approaches to cloud system design in order to choose an appropriate design to resolve cloud computing problems
  5. Describe the key underlying technologies that enable cloud computing that includes data centre infrastructures, virtualization and containerization, and automation and orchestration
  6. Design data centres using emerging networking technologies that include Software-Defined Networking (SDN) and Network Function Virtualization (NFV)


Overview of Learning Activities

The learning activities included in this course are:

  • Key concepts and basic principles will be explained in lectorials with current industry examples of applications and solutions.
  • Tutorials and labs will help students learn the tools and techniques, such as how to use cloud computing tools offered by industry leaders such as Amazon, and practice report writing by analysing case studies.
  • Project-based learning, where students will work in a group to analyse and solve industry-related problems with cloud computing solutions
  • Private study, working through the course as presented in classes and learning materials, group discussions (including online forums), and gaining practice at solving conceptual and technical problems.  


Overview of Learning Resources

You should make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through Canvas. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

The course is supported by the Canvas learning management system which provides specific learning resources.

See the RMIT Library Guide at http://rmit.libguides.com/compsci  


Overview of Assessment

Note: This course has no hurdle requirements.

Assessment Task 1: Project - AWS Cloud System Development
Weighting: 40%
This assessment task supports CLOs 1 - 4

Assessment Task 2: Written assignment 1
Weighting 30%
This assessment task supports CLO 4, 5

Assessment Task 3: Written assignment 2
Weighting 30%
This assessment task supports CLO 4 - 6

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.