Cluster and Cloud Computing (COMP90024)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
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- Dates and times
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Prof Richard Sinnott
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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).
- 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
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