93468 - Distributed Software Systems

Learning outcomes

The course presents some current technologies to develop distributed systems. The concept of distributed system is introduced first, then the concept of middleware technology as a distributed programming platform. The fundamental principles of distributed systems will be explained. This introductory material is followed by a description of the main architectures of distributed systems, including those for implementing and deploying service-based applications in the cloud. At the end of the course the students will be able to select the appropriate middleware platform for a given problem and to apply the main technologies presented to solve practical case studies. The exam will consist of 1) a midterm presentation on a research paper chosen by the student and 2) a final project assigned by the instructor.

Course contents

This course presents the fundamental ideas on distributed computing systems and their principles of construction and analysis

We also study the most common middleware technologies and the related methods for distributed programming, including protocols to have LLM be able to cooperate and coordinate (eg. MCP)

Goal: Understand principles and concepts underlying distributed computing: communication, concurrency, coordination and related algorithms

Goal: Learn how to design simple reliable distributed applications using (open source) middleware

Goal: Gain practical experience on the development of simple distributed systems (eg. in Java orPython)

Prerequisite: Be a graduate student – this is an advanced course.

You should have passed exams of: Operating systems, Networking, Software engineering.

Readings/Bibliography

vanSteen Tanenbaum Distributed Systems, 4ed, 2023

Gorton, Foundations of scalable systems, O’Reilly, 2022

Teaching methods

Lectures (1/3 of lectures will be online)

Hands-on homework assigned weekly

Midterm lecture on a topic chosen by the student

Assessment methods

Hands on homework  10% each (please present your result of at least 5 labs) – max 50%

Midterm report & presentation: 50%

Attendance: +/- bonus up to 10%
(negative bonus if you do not attend classes or get too many late deliveries of labs)

Teaching tools

Java or Python plus middleware libraries

Open source software to run on a distributed platform (Eg Kafka, Pulsar, etc)

LaTeX/Overleaf for preparing reports

Office hours

See the website of Paolo Ciancarini