Course: Advanced cloud architectures and data analysis tools for Smart Cities
Credits: Ph.D. Students, fall semester, 20 hours
Teacher: Roberto Girau - email:
Ufficio: DIEE pad A, piano rialzato.   Tel: 070-675-5903
Web site: MCLab web site
  • Classes for the 2020 course will start in the first week of september
  • Prospective students should register sending an email to the instructor by September 1.
  • The course is in English, but it can be given in Italian according to the students' needs.
The course is focused on the recent research achievement on smart cities architectural design and exploitation. The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standardization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, micro service architecture style has emerged and gained a lot of popularity in the industry in recent years. In this course, we show the benefits of applying the micro service architecture style to design a Smart City IoT platform and how this paradigm can be exploited to dynamically use advanced data analysis tools.
  • Topic 1: Cloud Computing model services (2 hours)
    • introduction;
    • emerging trends and practices for smart cities.
  • Topic 2: Microservice architecture (6 hours)
    • from monolith to microservice
    • microservice design principles;
    • The Netflix’s case.
  • Topic 3: Container with Dockers (4 hours)
    • containers described;
    • Docker basics;
    • Docker images.
  • Topic 4: Kubernetes (4 hours)
    • Introduction to deploying with Kubernetes;
    • Kubernetes deployment models and hosted solutions;
    • practical exercises.
  • Topic 5: Deploying data analysis tools with Docker and Kubernetes (4 hours)
final test