Computing Systems and Infrastructures

Recommended Prerequisites

The following curricular units are considered fundamental pre-requisites: IPRP / POO, SGD, SD. Fluency in English level B2 (ideally C1), according to the Common European Framework of Reference for Languages.

Teaching Methods

Lecture classes (T): presentation and discussion around the topics of the course.

Lab classes (PL): application of theoretical concepts in projects.

Learning Outcomes

This course intends to provide students with know-how about cloud technologies, from the perspective of a data scientist. In this sense, the proposed course syllabus includes topics such as virtualization or containers, organized towards a DevOps perspective. Ultimately, it is intended for students to learn and understand how to articulate and configure these resources according to specific reliability, resource elasticity and performance requirements.

Work Placement(s)

No

Syllabus

1. Cloud communications and computing: beyond the bare metal (VMs, containers, unikernels)

2. Cloud paradigms and related technologies

3. Private, public and hybrid clouds

4. Cloud service models

5. Understanding virtual resources for communications, storage and computing

6. DevOps for data scientists

7. Optimizing data ingestion and processing on massively distributed architectures

8. Parallel programming

9. Analysis of real-world use case scenarios

Head Lecturer(s)

David Alejandro Perez Abreu

Assessment Methods

Assessment
Project: 25.0%
Laboratory work or Field work: 25.0%
Exam: 50.0%

Bibliography

- Artigos, recursos disponíveis na Internet e capítulos de livros seleccionados, para cada tópico especializado.

- G. Santana, Data Center Virtualization Fundamentals: Understanding Features, Designs, and Techniques for Highly Efficient Data Centers (2013)

-Jennifer Davis, Ryn Daniels -  Effective DevOps Building a Culture of Collaboration, Affinity, and Tooling at Scale (2016)

-Kief Morris, Infrastructure as Code (2016)

-Rafal Leszko, Continuous Delivery with Docker and Jenkins: Delivering software at scale (2017)