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COMP5349: Cloud Computing (2019 - Semester 1)

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Unit: COMP5349: Cloud Computing (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Computer Science
Unit Coordinator/s: Dr Zhou, Ying
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing' and 'Big Data'.

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 Amazon, Microsoft, Google, Salesforce 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.

The unit covers the important enabling technologies of cloud computing and explores the state-of-the art platforms and the existing services. In terms of cloud applications, the unit focuses on a particular area: big data analytics and storage. The unit will explore a few influential big data frameworks and give student hands on experience on various types of big data workloads. Through out the semester, students are expected to develop broad knowledge in the cloud computing area, to experience a range of cloud services and to build solid skills on big data analytics.
Assumed Knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA
Lecturer/s: Dr Zhou, Ying
Tutor/s: Xiang Dai, Chenhao Huang, Heming Ni, Givanna Putri, Omid Tavallaie and Andrian Yang
Timetable: COMP5349 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Practical Labs 2.00 1 8
3 Independent Study 6.00 12
4 Project Work 3.00 8
T&L Activities: This subject uses a combination of weekly readings, lectures, practical labs and a practical assignment.

Students are expected to read the given weekly research papers before the lecture.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 3)
1. On successful completion of this unit, a student should be able to describe and analyze the execution plan of various big data workloads.
(2) Engineering/ IT Specialisation (Level 4)
2. On successful completion of this unit, a student should be able to describe the fundamental techniques in cloud computing such as data center infrastructures, virtualization and container technology, partitioning, replication and fault tolerance.
3. On successful completion of this unit, a student should be able to describe and compare key principles and implementation details of cloud services like infrastructure, platform, storage and software services.
4. On successful completion of this unit, a student should be able to describe resource scheduling at various levels, e.g. VM, container and programming
5. On successful completion of this unit, a student should be able to explain various algorithms for distributed data consistency such as 2PC and Paxos
6. On successful completion of this unit, a student should be able to describe the core security and data privacy issues for data processing with cloud infrastructures
(3) Problem Solving and Inventiveness (Level 4)
7. On successful completion of this unit, a student should be able to design and implement big data analytic workload using various frameworks
8. On successful completion of this unit, a student should be able to apply functional programming paradigm to design big data analytic workload
9. On successful completion of this unit, a student should be able to analyze the execution performance of big data analytic workload based on hardware configuration and parameter setting
10. On successful completion of this unit, a student should be able to evaluate the performance of various algorithms on a specific analytic workload
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Simple Data Analytics Project No 20.00 Week 6 1, 7, 8,
2 Advanced Data Analytics Project Yes 20.00 Week 11 1, 7, 8, 9, 10,
3 Written Examination No 60.00 Exam Period 2, 3, 4, 5, 6, 8, 9,
Assessment Description: The Simple Data Analytics project is designed to help students understand the basic operations of big data framework. The data set involved is relatively small. The analysis can be carried out in a single machine setting. Students are expected to complete the project individually.

The Advanced Data Analytics Project is designed to test students` ability to carry out more complex analytics on large data set. The analytics will involve certain machine learning algorithms and should be carried out on a cluster setting. Though group work, each team member is expected to be able to discuss their part of the solution during the tutorial to the tutor.

Students are expected to use a version control system, preferably the university`s enterprise GitHub code repository to share and distribute code in both projects.

The late penalty for both projects is 10% of the awarded mark per day late; maximum 7 days late (after that: 0).

The cloud computing concepts will be assessed in a 2 hour written final exam in the examination period.

You must get 40% in the final exam to pass the unit, regardless of the sum of your individual marks.

There may be statistically defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes.
Assessment Feedback: Feedback on the progress of the projects will be given throughout the semester in the tutorial after the lecture.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement 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.
Policies & Procedures: 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.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Online Course Content: On the unit`s eLearning site (Blackboard), there will be available the lecture slides, weekly readings, lab handouts and any background information.

The discussion forum will be set up on the piazza.com system, on which students are encouraged to actively participate.
Note on Resources: Each week's topic will be based on two or three research papers which students are expected to read and which will be linked on the unit's eLearning site (as well as on piazza). Additionally, we will use online manuals for the Hadoop and the HBase systems.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Cloud Computing and Datacenter Overview
Week 2 Virtualization Technology
Week 3 Container Technology
Week 4 HDFS and MapReduce
Week 5 Spark Programming
Week 6 YARN scheduling
Assessment Due: Simple Data Analytics Project
Week 7 MapReduce Design Patterns
Week 8 Spark Machine Learning
Week 9 Spark Machine Learning II
Week 10 Cloud Storage
Week 11 Cloud Application
Assessment Due: Advanced Data Analytics Project
Week 12 Benchmarking Cloud Services
Week 13 Unit of Study Review
Exam Period Assessment Due: Written Examination

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019
Bachelor of Advanced Computing (Software Development) 2018, 2019
Bachelor of Computer Science and Technology (Honours) 2015, 2016, 2017
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Graduate Certificate in Information Technology 2015, 2016, 2017, 2018, 2019
Graduate Certificate in Information Technology Management 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Computing 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Health Technology Innovation 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Information Technology Management 2015, 2016, 2017, 2018, 2019
Graduate Certificate in Information Technology (till 2014) 2014
Graduate Diploma in Information Technology (till 2014) 2014
Master of Data Science 2016, 2017, 2018, 2019
Master of Health Technology Innovation 2015, 2016, 2017, 2018, 2019
Master of Information Technology 2015, 2016, 2017, 2018, 2019
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019
Master of IT/Master of IT Management 2015, 2016, 2017, 2018, 2019
Master of Information Technology (till 2014) 2014

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 3) No 10%
(8) Professional Effectiveness and Ethical Conduct (Level 4) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 4) No 48%
(3) Problem Solving and Inventiveness (Level 4) No 42%
(1) Maths/ Science Methods and Tools (Level 4) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.