Cloud Computing for Science

Cloud Computing for Science

Academic year 2020/2021

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

This course aims at introducing the student to the key concepts and technologies of current cloud computing systems.

Results of learning outcomes

Students will learn both theoretic and practice concepts concerning Cloud Computing so as they will be able to realize in which scientific scenarios this computing paradigm can be fully exploited.

Program

The course introduces the student to cloud computing. It surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It consists of two parts:

• Part 1: basic concepts and practice. This part will present the main aspects of cloud computing discussing the pros and cons of this recent computational platform. The student will also get practice with the main Cloud Computing platforms such as Amazon Web Services, Google Cloud Platform and OpenStack.
• Part 2: systems and architectures for big data. This part will present the key principles, paradigms, tools and technologies underlying distributed systems and architectures for big data analytics services and applications (e.g., batch and stream data processing). Practical examples will be provided mainly based on the Apache Hadoop ecosystem (e.g., MapReduce and Spark).

There are no required textbooks for this course. The lecturers will propose papers, documentation and websites as educational materials during the course.

Class schedule

Note

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums