Note: This unit will only have online classes in 2022. All lectures and tutorials will be available online only.
In recent years, due to advancement of internet technologies and instrumentation of every part of our life, we have noticed a huge surge in data available to us. This revolution is termed as Big Data. This Big Data cannot be processed or managed by any traditional methods of processing. This has led to development of several high performance and distributed computing platforms and programming frameworks. The design of such platforms relies on distributed computing concepts which are implemented in the form of systems such as Clusters and Clouds, and Big Data frameworks such as MapReduce and Stream Computing. These systems play an important role in todays' research, academia or industries by providing the processing of data generated from a variety of networked resources, e.g. large data stores and information repositories, expensive instruments, social media, sensors networks, and multimedia services for a wide range of applications.
The aim of this unit is to provide students with the foundation knowledge and understanding of Big Data and distributed computing systems and applications especially in context of Cloud. In other words, this unit will equip students with essential knowledge that is needed for building next-generation applications that are scalable and efficient and can process Big Data.
Key topics that will be covered: parallel systems: parallel paradigms, parallel and distributed algorithms, and building parallel applications using MPI; Cluster computing: cluster fundamentals and architecture; Big Data: MapReduce platforms, Stream Computing platforms and Algorithms. Cloud computing: Cloud technologies, virtualization, programming model, resource management and scheduling, application building for managing and analyzing data. The unit will also explain how the business models of enterprises are changing with these forms of computing that provide large storage and computation space without purchasing expensive computer systems.
|Unit name||Big Data and Cloud Computing|
|College/School||College of Sciences and Engineering
School of Information and Communication Technology
|Discipline||Information & Communication Technology|
|Coordinator||Doctor Saurabh Garg|
|Available as student elective?||Yes|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
|Study Period||Start date||Census date||WW date||End date|
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Unit census dates currently displaying for 2022 are indicative and subject to change. Finalised census dates for 2022 will be available from the 1st October 2021.
- analyse the problems and challenges associated with building distributed computing applications;
- adapt the emerging big data and cloud technologies informed by an appropriate evaluation process, to support business applications.
- Design high performance and cloud applications to support scalable online services.
- Design big data processing applications to efficiently process high volume and velocity data
|Field of Education||Commencing Student Contribution 1||Grandfathered Student Contribution 1||Approved Pathway Course Student Contribution 2||Domestic Full Fee|
- Available as a Commonwealth Supported Place
- HECS-HELP is available on this unit, depending on your eligibility3
- FEE-HELP is available on this unit, depending on your eligibility4
1 Please refer here more information on student contribution amounts.
2 Information on eligibility and Approved Pathway courses can be found here
3 Please refer here for eligibility for HECS-HELP
4 Please refer here for eligibility for FEE-HELP
Please note: international students should refer to this page to get an indicative course cost.
PrerequisitesKIT205 OR KIT206 OR KIT214
Lectures: 3 hr/wk
|Assessment||Tutorial work (10%)|Examination - invigilated (externally - Exams Office) (50%)|Multithreaded Server (10%)|Big Data Assignment-Hadoop (10%)|Cloud Based Processing System (20%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
|Links||Booktopia textbook finder|
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