Cluster and Cloud Computing (COMP90024)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 1
Prof Richard Sinnott
email: rsinnott@unimelb.edu.au
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
AIMS
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). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains.
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, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis.
This subject will enable students to understand these technologies, their 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. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).
INDICATIVE CONTENT
- Cluster computing: elements of parallel and distributed computing, cluster systems architecture, resource management and scheduling, single system image, parallel programming paradigms, cluster programming with MPI
- Utility computing: foundations and grid computing technologies
- Cloud computing: cloud platforms, Virtualization, Cloud Application Programming Models (Task, Thread, and MapReduce), Cloud applications, and future directions in utility and cloud computing
- "Big data" processing and analytics in distributed environments.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- 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
Generic skills
On completion of this subjects students should have the following skills:
- 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.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
ONE OF:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP90041 | Programming and Software Development |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP90038 | Algorithms and Complexity |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP90007 | Internet Technologies |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
OR
admission into one of the following courses:
- MC-ENG Master of Engineering, all entry points
- MC-IS Master of Information Systems, 100 or 150 point program
- MC-IT Master of Information Technology, 100 or 150 point program
- MC-SCICMP Master of Science (Computer Science)
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
- Individual Cluster Computing Assignment, requiring approximately 25 - 30 hours programming and 2000 word report (10%)
- Group-based Cloud programming assignment system, requiring approximately 50-55 hours programming and 5000 word report (40%)
- A 2 hour end-of-semester written examination (50%).
Hurdle requirement: 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.
Intended Learning Outcome (ILO) 1 is addressed in all assessment components. ILO 2 is addressed in the project work, ILO 3 in the first assignment.
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Richard Sinnott Mode of delivery On Campus (Parkville) Contact hours 3 hours per week Total time commitment 200 hours Teaching period 4 March 2019 to 2 June 2019 Last self-enrol date 15 March 2019 Census date 31 March 2019 Last date to withdraw without fail 10 May 2019 Assessment period ends 28 June 2019 Semester 1 contact information
Prof Richard Sinnott
email: rsinnott@unimelb.edu.au
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
The subject will be delivered through a combination of lectures and both individual and team-based learning. In team-based learning, a group of students will jointly develop applications.
INDICATIVE KEY LEARNING RESOURCES
Students will have access to lecture notes and lecture slides. The subject LMS site also contains links to recommended literature and current survey papers of cluster and cloud computing principles.
CAREERS / INDUSTRY LINKS
Adoption of the technologies taught in this subject, and in particular cloud computing, is growing quickly. All the big players in the ICT market offer at least one product that is based on these technologies. Therefore, there are many opportunities for professionals that understand them and are able to develop applications and support software for them. Some examples of commercial companies playing a major role in Cloud computing area are: Amazon, IBM, Microsoft, Google, Oracle, CA, VMWare, and Citrix. The area of "Big data" is also one of the "hot topics" in great demand in the industry at present.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science Course Ph.D.- Engineering Course Master of Philosophy - Engineering Course Doctor of Philosophy - Engineering Course Master of Science (Computer Science) Specialisation (formal) Distributed Computing Specialisation (formal) Software Specialisation (formal) Mechatronics Major Computer Science - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 3 November 2022