Algorithms for Massive Data, Cloud and Distributed Computing

A.Y. 2019/2020
Lesson for
12
Max ECTS
80
Overall hours
SSD
INF/01
Language
English
Learning objectives
The main objective of the course is to analyze the technologies, computing paradigms, models, and algorithms at the basis of massive data management and analysis. On the one hand, students will learn the main approaches enabling them to process massive amounts of data; on the other one, they will also analyze the modern distributed computing systems, including cloud computing and microservice-based architectures. At the end of the course, students will be able to design and execute computations on massive datasets, deployed on modern distributed systems and cloud computing platforms; moreover, they will learn the fundamentals of non-functional property (e.g., privacy) management in the cloud. To achieve the above mentioned objectives, the course will consist of two modules: i) "Algorithm for Massive Data" (40 hours - 6 CFU), and ii) "Cloud and Distributed Computing" (40 hours - 6 CFU).

Course structure and Syllabus

Active edition
Yes
Module Algorithms for Massive Data
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor: Malchiodi Dario
Module Cloud and Distributed Computing
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
ATTENDING STUDENTS
Module Cloud and Distributed Computing
Syllabus
The module will discuss the technologies and solutions at the basis of cloud computing and modern distributed systems, including microservice architectures. It is composed of three main parts as follows.

After a brief recall of the fundamentals of IT networks and virtualization, the first part of the module will provide an overview of the cloud computing paradigm and its technologies, as well as its service and deployment models. It will also investigate risks and opportunities of cloud migration, focusing on governance and non-functional properties of the cloud.

1. Cloud Computing. Service models. Deployment models. Migration to the cloud. Cloudonomics. Challenges and issues.
2. IaaS, PaaS, SaaS: Definitions. Technologies. Case studies.
3. Non-functional aspects of the cloud.
4. PaaS Big Data. Multicloud orchestration. Big Data analytics examples.

The second part of the module will provide an overview of the microservice architecture and its technologies, focusing on the migration from a monolithic approach to microservices and on microservice orchestration.

1. Microservice architecture. Overview and basic concepts. Microservices and containers. Dockers.
2. Microservice migration and orchestration. Cloud for microservices. How to migrate a monolithic software to microservices. Examples.
3. New cloud services: AWS, Azure, Soft-Layer/BlueMix e GCP
4. Microservices and Big Data. Model-Based Big Data Analytics-as-a-Service.

Finally, the third part of the module will focus on privacy and data protection in the cloud.

1. Data and access confidentiality and integrity in outsourcing and cloud scenarios.
NON-ATTENDING STUDENTS
Module Cloud and Distributed Computing
Syllabus
The module will discuss the technologies and solutions at the basis of cloud computing and modern distributed systems, including microservice architectures. It is composed of three main parts as follows.

After a brief recall of the fundamentals of IT networks and virtualization, the first part of the module will provide an overview of the cloud computing paradigm and its technologies, as well as its service and deployment models. It will also investigate risks and opportunities of cloud migration, focusing on governance and non-functional properties of the cloud.

1. Cloud Computing. Service models. Deployment models. Migration to the cloud. Cloudonomics. Challenges and issues.
2. IaaS, PaaS, SaaS: Definitions. Technologies. Case studies.
3. Non-functional aspects of the cloud.
4. PaaS Big Data. Multicloud orchestration. Big Data analytics examples.

The second part of the module will provide an overview of the microservice architecture and its technologies, focusing on the migration from a monolithic approach to microservices and on microservice orchestration.

1. Microservice architecture. Overview and basic concepts. Microservices and containers. Dockers.
2. Microservice migration and orchestration. Cloud for microservices. How to migrate a monolithic software to microservices. Examples.
3. New cloud services: AWS, Azure, Soft-Layer/BlueMix e GCP
4. Microservices and Big Data. Model-Based Big Data Analytics-as-a-Service.

Finally, the third part of the module will focus on privacy and data protection in the cloud.

1. Data and access confidentiality and integrity in outsourcing and cloud scenarios.
Lesson period
Second semester
Lesson period
Second semester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi
Professor(s)
Reception:
By appointment only
At Dipartimento di Informatica (Room BP71) in via Bramante 65, Crema (CR)
Reception:
By appointment
Room 5015, Dipartimento di Informatica