Outline

Overview

This unit will provide a 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 distance-vector routing protocol; 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).

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(INFO1105 or INFO1905) or ((INFO1103 or INFO1113) and (COMP2123 or COMP2823))
Corequisites ?	None
Prohibitions ?	COMP2121
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Vincent Gramoli, vincent.gramoli@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam CC students: Pen and paper on campus.	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Programming assignment.	20%	Week 05 Due date: 28 Mar 2025 at 23:59	2-3 weeks
Outcomes assessed:
Assignment	Assignment 2 Programming assignment.	30%	Week 11 Due date: 16 May 2025 at 23:59	4-5 weeks
Outcomes assessed:
= hurdle task ? = AI allowed ?

Assessment summary

Assignment 1 – writing a computer program to solve a given task.
Assignment 2 – writing a computer program to solve a given task.
Exam – 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	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.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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:

For all assignments: a penalty of 5% per day late will apply. Late submissions will not be accepted after 5 days late.

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.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction	Lecture (2 hr)
Week 02	Architecture	Lecture and tutorial (2 hr)
Week 02	Tutorial: Multi-Threading	Lecture and tutorial (1 hr)
Week 03	Communication (Routing)	Lecture and tutorial (2 hr)
Week 03	Tutorial: Routing	Lecture and tutorial (1 hr)
Week 04	Communication (TCP) and Naming	Lecture and tutorial (2 hr)
Week 04	Tutorial: Client-Server Communication	Lecture and tutorial (1 hr)
Week 05	Synchronisation	Lecture and tutorial (2 hr)
Week 05	Tutorial: Synchronisation	Lecture and tutorial (1 hr)
Week 06	Consistency	Lecture and tutorial (2 hr)
Week 06	Tutorial: Consistency	Lecture and tutorial (1 hr)
Week 07	Blockchain	Lecture and tutorial (2 hr)
Week 07	Tutorial: Blockchain	Lecture and tutorial (1 hr)
Week 08	Consensus	Lecture and tutorial (2 hr)
Week 08	Tutorial: Consensus	Lecture and tutorial (1 hr)
Week 09	Security	Lecture and tutorial (2 hr)
Week 09	Tutorial: Security	Lecture and tutorial (1 hr)
Week 10	Robustness	Lecture and tutorial (2 hr)
Week 10	Tutorial: Replication	Lecture and tutorial (1 hr)
Week 11	Scalability	Lecture and tutorial (2 hr)
Week 11	Tutorial: Robustness	Lecture and tutorial (1 hr)
Week 12	Other challenges	Lecture and tutorial (2 hr)
Week 12	Tutorial: Demo or Q/A as needed	Lecture and tutorial (1 hr)
Week 13	Review	Lecture and tutorial (2 hr)

Attendance and class requirements

The students are expected to attend the lectures physically as the recording are not guaranteed and engagement would be beneficial to everyone.

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

The readings for this unit can be accessed through the Library's reading list on Canvas.

- Blockchain Scalability and its Foundations in Distributed Systems. V. Gramoli. Springer, ISBN:978-3-031-12577-5, 2022.

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

Learning outcomes

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.

Outcomes
Graduate qualities

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 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
Learning outcomes

Responding to student feedback

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

We have integrated EdStem for programming assignments and labs. The blockchain assignment has been changed and the former 2nd assignment has been dropped to leave room to work on the blockchain assignment. A couple of important topics that have been missing have been added back to the lectures and tutorials.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP3221: Distributed Systems

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links