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Unit outline_

COMP5349: Cloud Computing

Semester 1, 2022 [Normal day] - Remote

This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'. Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud. In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The unit will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Basic knowledge of computer networks as covered in INFO1112 or COMP9201 or COMP9601 (or equivalent UoS from different institutions)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Ying Zhou, ying.zhou@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam hurdle task Written examination
Written examination
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Assignment Simple Analytics with Spark
Programming + report writing
20% Week 07 n/a
Outcomes assessed: LO6 LO7
Assignment Data analytics project
Programming + Report Writing
20% Week 12 n/a
Outcomes assessed: LO1 LO6 LO7 LO8 LO9
hurdle task = hurdle task ?
Type D final exam = Type D final exam ?

Assessment summary

  • Simple Analytic with Spark:  The assignment test your ability to write Spark program using RDD API to handle simple data analytic tasks on a small data set. The execution is expected to run on a single machine. 
  • Data analytics project: The advanced data analytics project is designed to test your ability to carry out more complex analytics on large data set. The analytics may involve machine learning algorithms and should be carried out on a cluster setting.
  • Written examination: The final exam will be carried out in 3 hour take home exam format in the examination period. You must get 40% in the final exam to pass the unit, regardless of the sum of your individual marks.
Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date: Deduction of 5% of the maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Cloud computing and datacenter overview Lecture (2 hr)  
Introduction to GIT (optional) Computer laboratory (2 hr)  
Week 02 Virtualization technology Lecture (2 hr)  
Virtualization technology Computer laboratory (2 hr)  
Week 03 Container technology Lecture (2 hr)  
Container technology Computer laboratory (2 hr)  
Week 04 MapReduce Framework Lecture (2 hr) LO6
MapReduce Lab Computer laboratory (2 hr) LO6
Week 05 Spark programming Lecture Lecture (2 hr) LO6 LO7
Spark programming Lab Computer laboratory (2 hr) LO6 LO7
Week 06 HDFS and YARN Lecture Lecture (2 hr) LO4
Spark Programming II Computer laboratory (2 hr) LO4
Week 07 Spark on YARN Lecture (2 hr) LO1 LO4
YARN execution Computer laboratory (2 hr) LO7 LO8
Week 08 Spark Data Frame Lecture (2 hr) LO6 LO7
Spark Data Frame Computer laboratory (2 hr) LO6 LO7 LO8
Week 09 Spark machine learning Lecture (2 hr) LO6
Spark machine learning Computer laboratory (2 hr) LO6 LO8
Week 10 Cloud storage Lecture (2 hr) LO3
Cloud storage lab Tutorial (2 hr) LO2 LO3
Week 11 Cloud consistency Lecture (2 hr) LO5
Cloud consistency lab Tutorial (2 hr) LO5
Week 12 Kubernate Lecture (2 hr) LO3
Week 13 Unit of study review Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. describe and analyze the execution plan of various big data workloads
  • LO2. describe the fundamental techniques in cloud computing such as data center infrastructures, virtualization and container technology, partitioning, replication and fault tolerance
  • LO3. describe and compare key principles and implementation details of cloud services like infrastructure, platform, storage and software services
  • LO4. describe resource scheduling at various levels, e.g. VM, container and programming
  • LO5. explain various algorithms for distributed data consistency such as 2PC and Paxos
  • LO6. design and implement big data analytic workload using various frameworks
  • LO7. apply functional programming paradigm to design big data analytic workload
  • LO8. analyze the execution performance of big data analytic workload based on hardware configuration and parameter setting
  • LO9. evaluate the performance of various algorithms on a specific analytic workload.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

The final exam duration has been set to standard 2 hours exam.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.