Large Scale Distributed Systems

Code:

M.EIC004

Acronym:

SDLE

Keywords
Classification	Keyword
OFICIAL	Computer Architecture, Operating Systems and Networks

Instance: 2021/2022 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EIC	136	Syllabus	1	-	6	52	162

Teaching language

Suitable for English-speaking students

Objectives

This course unit has two main objectives:

1.  give students theoretical knowledge on distributed systems so they can make correct decisions when faced with the need to design/develop/administer large-scale systems;
2. provide students with practical experience so they can develop applications using techniques and mechanisms appropriate for large-scale systems.

Learning outcomes and competences

Upon conclusion of this course, the students should be able to:

• explain the importance of distribution and of the coordination models in the scalability of applications and services
• identify the main challenges of distribution and model them in abstract terms
• assess the different techniques and algorithms used to ensure scalability and availability
• assess the different techniques and algorithms used to ensure fault-tolerance at large scale
• design a large-scale application/service, given its specification
• implement, integrate, and run large-scale services and applications, using selected technologies.
• have skills for identifying the state-of-the-art

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Students are expected to have passed courses on Operating Sytems, Computer Networks and Distributed Systems.

Program

Scalable Distributed Topologies
System Design for Large Scale
Cloud and Datacenter architectures
Implementation techniques for scalability
Events, Physical Time, and Logical Time
Data Consistency Models
Fault-tolerance for Large Scale

Mandatory literature

M. van Steen and A. S. Tanenbaum; Distributed systems, 3rd Ed., 2017 (https://www.distributed-systems.net/index.php/books/ds3/)

Complementary Bibliography

G. Coulouris, J. Dollimore, T. Kindberg and G. Blair; Distributed Systems: Concepts and Design, 5th Ed., Pearson, 2012. ISBN: 978-0132143011
L. Barroso, U Hölze and P. Ranganathan; The Datacenter as a Computer, 3rd Ed., Morgan & Claypool, 2019. ISBN: 9781681734361

Teaching methods and learning activities

All topics are covered in the lectures, which are mostly expository. To motivate the students, we use case studies, real-world examples and demos, as appropriate.

To enhance the understanding of the concepts and algorithms presented in the lectures, the students will perform a project as well as smaller assignments.

Software

NetworkX
Git
gcc
Rust
JDK
Go
Python

keywords

Technological sciences > Technology > Computer technology > Software technology

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	45,00
Trabalho prático ou de projeto	45,00
Participação presencial	10,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	50,00
Frequência das aulas	52,00
Trabalho laboratorial	60,00
Total:	162,00

Eligibility for exams

In order to be able to take the final exam students must:
1) Attend at least 75% of the scheduled classes;
2) Have a minimum grade of 10 (out of 20) in the weighted average of the projects/assignments;
3) Demonstrate the final project
4) Fill the peer evaluation forms of all projects/assignments;

Calculation formula of final grade

min( 0.45 Proj + 0.45 Ex + 0.1 CP, Proj + 3, Ex. + 3)

where:
Proj -- a weighted average of the projects/assignments grades
Ex -- final exam grade
CP -- class participation

For passing, students must have a minimum grade of 10 (out of 20) in both the weighted average of the projects and the final exam.

Special assessment (TE, DA, ...)

The same as for ordinary students.

Classification improvement

The final exam grade can be improved in the scheduled exams.
The assignments and project grades can be improved only in other instances of the course unit.

Observations

Content is in English. The course will be taught in English.

Lecture's URL:

https://teams.microsoft.com/l/meetup-join/19%3a6XPHZln1S2S0o5lOSn5uxPHop06zC-lBnUfHTxZhBLM1%40thread.tacv2/1634201420813?context=%7b%22Tid%22%3a%22b7821bc8-67cc-447b-b579-82f7854174fc%22%2c%22Oid%22%3a%22bcc8d252-1986-4bf2-b459-af4901f368ff%22%7d