Cloud Computing

This subject has not requirements
Cloud computing is a service model for large-scale distributed computing based on a converged infrastructure and a set of common services over which applications can be deployed and run over the network. This course about Cloud computing is divided into two main parts, one centered in the key concepts behind the Cloud paradigm and another more practical that deals with the related technologies.

In the first part of this course, the students will learn the principles and the state of the art of large-scale distributed computing in a service-based model. They will look at how scale affects system properties, models, architecture, and requirements. In terms of principles, the course looks at how scale affects systems properties, issues (such as virtualization, availability, locality, performance and adaptation), system models (game-theoretic, economic, evolutionary, control, complexity), architectural models (multi-tier, cluster, cloud), environment and application requirements (such as fault tolerance, content distribution). The first part of this course also reviews the state of the art in resource management of Cloud environments (composed of different types of platforms and organization) to support current applications and their requirements.

In the second part of the course the students will gain a practical view of the latest in Cloud technology in order to implement a prototype that meets a business idea created by a student. In this part the students will begin creating a basic toolbox to get started in the Cloud. This will prepare them to practice with APIs, the doors in the Cloud. All these things together will allow the students to mine the deluge of data coming from the Cloud or use new advanced analytics services provided nowadays by the Cloud. Finally we will look under the hood of these advanced analytics services in the Cloud, either in terms of software or hardware, in order to understand, how their high performance requirements can be provided.

Weekly hours

Guided learning
Autonomous learning


Technical Competences of each Specialization

Computer networks and distributed systems

  • CEE2.1 - Capability to understand models, problems and algorithms related to distributed systems, and to design and evaluate algorithms and systems that process the distribution problems and provide distributed services.
  • CEE2.3 - Capability to understand models, problems and mathematical tools to analyze, design and evaluate computer networks and distributed systems.

High performance computing

  • CEE4.3 - Capability to analyze, evaluate, design and manage system software in supercomputing environments.

Generic Technical Competences


  • CG4 - Capacity for general and technical management of research, development and innovation projects, in companies and technology centers in the field of Informatics Engineering.

Transversal Competences

Entrepreneurship and innovation

  • CTR1 - Capacity for knowing and understanding a business organization and the science that rules its activity, capability to understand the labour rules and the relationships between planning, industrial and commercial strategies, quality and profit. Capacity for developping creativity, entrepreneurship and innovation trend.


  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.


  1. The goal of this course is to introduce new execution environments needed to manage the computing resources and simplify the development and integration of the different types of applications and services at today Internet-scale systems.
    Related competences:


  1. PART I: Cloud Computing fundamentals
    Fundamental concepts: The effect of scale in system properties.

    ---- Issues in large-scale systems: virtualization, service orientation and composition, availability, locality, performance and adaptation.

    ---- Models for large-scale systems: system models for analysis, architectural models and service/deployment models.

    ---- Scaling techniques: basic techniques, scalable computing techniques for architectural models.

    ---- Middleware and Applications: computing, storage, web, content distribution, Internet-scale systems or services.

    ---- Environment and applications requirements.
  2. PART II: Practical view of Cloud Computing
    Big Data Analytics in the Cloud
    ---- APIs: The Doors in the Cloud
    ---- Current required layers in Big Data Software Stack
    ---- New Software requirements for Advanced Analytics
    ---- New Hardware requirements for Advanced Analytics
  3. PART III: Guest Lectures
    Invited lectures from the industry and academia will illustrate and describe specific aspects. The content will vary depending on visits and availability of invited speakers.
  4. PART IV: Experimental part
    Development of a prototype application using Cloud service offerings (such as AWS, Google AppEngine, Open Stack, OpenNebula.
    ---- Development of a prototype application using advanced analytics services either provided in terms of APIs or Software as a Service.

Teaching methodology

Theory classes, Reading and discussion of research papers, Presentation of topics (and papers) by students, Laboratory activities and Guest lectures

Evaluation methodology

The evaluation of the course will be based on the participation of students in class, lab sessions, class attendance, reading and presenting reports and papers and a projects work on specific topics.
The final grade for the course is the weighted average of the grades for the following components obtained en each part of the course:
· Lab sessions: 35%
· Paper Readings/Presentations and homework: 25%
· Course Projects: 15%
· Participation: 25% (Class participation 5% + Class attendance: 20%)