Cluster and Cloud Computing

Subject COMP90024 (2013)

Note: This is an archived Handbook entry from 2013.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2013:

Semester 2, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 3 hours per week
Total Time Commitment:

120 hours

Study Period Commencement:
Credit Points:
Not offered in 2013


Recommended Background Knowledge:


Non Allowed Subjects:


Core Participation Requirements:


Prof Richard Sinnott



Subject Overview:

The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). The PDC on local-area-networks is called "cluster computing " and wide-area networks is called "grid computing" . Clusters employ cost-effective commodity components for building powerful computers within local-area networks, and Grids allow to share and aggregate geographically distributed resources. Recently, “cloud computing” emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. This revolutionary new paradigm has its roots, and therefore shares many characteristics, with grids.

Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, bigdata computing, and delivering various business and consumer applications on a pay-as-you-go basis.

This subject will enable students to understand these technologies, its goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms.

This subject is an elective subject in the Master of Information Technology and a mandatory for the Distributed Computing Specialisation. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).


On completion of this subject students should:

  • Be able to understand emerging distributed technologies
  • Be able to design large-scale distributed systems
  • Be able to implement high-performance cluster and cloud applications
  • A small assignment including a report (10%) in the area of MPI programming on clusters.
  • Project work which includes a term paper, design and development of a system and around 5000 words report (40%)
  • A 3 hour end-of-semester written examination (50%)

To pass the subject students must obtain at least:

  • 25/50 in assignment/project work
  • And 25/50 in the end-of-semester written examination
  • ILO 1 is addressed in all assessment components. ILO 2 is addressed in the project work, ILO 3 in the first assignment.
Prescribed Texts:


Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On successful completion students should:

  • Have improved skills in teamwork and presentation of results
  • Be able to undertake problem identification, formulation and solution
  • Have a capacity for independent critical thought, rational inquiry and self-directed learning
  • Have a profound respect for truth and intellectual integrity, and for the ethics of scholarship
Related Course(s): Master of Engineering in Distributed Computing
Master of Information Technology
Master of Information Technology
Master of Information Technology
Master of Philosophy - Engineering
Master of Science (Computer Science)
Master of Software Systems Engineering
Ph.D.- Engineering
Postgraduate Certificate in Engineering
Related Majors/Minors/Specialisations: Computer Science
Master of Engineering (Software)

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