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Unit of study_

COMP3221: Distributed Systems

This unit will provide broad introduction to the principles of distributed computing and distributed systems and their design; provide students the fundamental knowledge required to analyse, design distributed algorithms and implement various types of applications, like blockchains; explain the common algorithmic design principles and approaches used in the design of message passing at different scales (e.g., logical time, peer-to-peer overlay, gossip-based communication).


Academic unit Computer Science
Unit code COMP3221
Unit name Distributed Systems
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

(INFO1105 OR INFO1905) OR ((INFO1103 OR INFO1113) AND (COMP2123 OR COMP2823))
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Nguyen Tran,
Type Description Weight Due Length
Final exam (Open book) Type C final exam hurdle task Exam
Canvas Online exam.
50% Formal exam period 2 hours
Outcomes assessed: LO2 LO7 LO6 LO5 LO4 LO3
Online task Mid-term quiz
Online quiz.
15% Week 07 Up to 1h
Outcomes assessed: LO2 LO4 LO5 LO6
Assignment Assignment 1
15% Week 08 3-4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Assignment 2
20% Week 12 3-4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
hurdle task = hurdle task ?
Type C final exam = Type C final exam ?
  • Assignment 1 – writing a computer program to solve a given task.
  • Assignment 2 – writing a computer program to solve a given task and a report discussing the results.
  • Mid-term quiz - small online quiz test to review mid-term contents. 
  • Exam – online exam at the end of the semester (less than 40% is automatically a FAIL)

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

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

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction Online class (2 hr) LO4
Week 02 Architecture Online class (2 hr) LO4
Tutorial: Multi-Threading Online class (1 hr) LO6
Week 03 Communication (Routing) Online class (2 hr) LO4
Tutorial: Routing Online class (1 hr) LO6
Week 04 Communication (TCP) and Naming Online class (2 hr) LO5
Tutorial: Client-Server Communication Online class (1 hr) LO6
Week 05 Synchronisation Online class (2 hr) LO4
Tutorial: Time Synchronization Online class (1 hr) LO4
Week 06 Consistency Online class (2 hr) LO4
Tutorial: Consistency Online class (1 hr) LO4
Week 07 Blockchain Online class (2 hr) LO4 LO6
Midterm Exam Online class (1 hr) LO2 LO3 LO4 LO5
Week 08 Fault tolerance Online class (2 hr) LO2 LO3 LO4
Tutorial: Concensus for Fault-Tolerance Online class (1 hr) LO3 LO4
Week 09 Distributed Machine Learning (Linear Regression) Online class (2 hr) LO4 LO7
Tutorial: Linear Regression Online class (1 hr) LO7
Week 10 Distributed Optimization Online class (2 hr) LO4 LO7
Tutorial: Distributed Optimization Online class (1 hr) LO7
Week 11 Distributed Logistic Regression Online class (2 hr) LO4 LO7
Tutorial: Logistic Regression Online class (1 hr) LO7
Week 12 Security Online class (2 hr) LO2 LO4
Tutorial: Security Online class (1 hr) LO2
Week 13 Review Online class (2 hr) LO4

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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Van Steen and Tanenbaum, Distributed Systems Principles and Paradigms, 2nd edition.

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. produce professional quality written assignments and reports as well as well-documented software for reuse
  • LO2. evaluate the implications of sharing of information and the importance of privacy and security as well as appreciate the importance of ethical behaviour among users of distributed systems
  • LO3. design efficient distributed algorithms and produce distributed software based on these designs
  • LO4. understand the general properties of distributed systems. You should be familiar with various types of distributed applications and how information is shared between distributed components
  • LO5. understand programming paradigms for distributed systems (e.g. sockets) and be able to apply them to protocols
  • LO6. implement distributed algorithms. You will be able to able to apply some common distributed algorithms (e.g. searches, shortest path, trees) towards solving problems.
  • LO7. understand and implement distributed and scalable machine learning algorithms for large-scale networks.

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
No changes have been made since this unit was last offered


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