High Performance Computer Architecture

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
AC
Web
https://ccbda-upc.github.io/mei/
Cloud computing is a service model for large-scale distributed computing based on concentrated infrastructure and a set of collaborative services over which applications can be deployed and run over the network.

This course about Cloud computing has a mainly practical approach dealing with the related technologies. While the explained concepts apply to any application, the classes pay particular attention to the creation of Big Data Analytics applications on the Cloud.

In the lectures 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. Students will study how scale affects system properties, models, architecture, and requirements.

Regarding principles, this course looks at how scale affects systems properties, issues (such as virtualization, availability, locality, performance, and adaptation), system models, architectural models, environment and application requirements (such as fault tolerance, content distribution). This course also reviews 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 laboratory sessions of this course, the students will gain a practical view of the latest in Cloud technology to implement a prototype that meets a business idea created by a student. The students will begin by building an essential toolbox to get started in the Cloud. They will later have 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, they will look under the hood of these high-level analytics services in the Cloud, either regarding software or hardware, to understand, how to meet high-performance requirements.

Teachers

Person in charge

  • Angel Toribio Gonzalez ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
7.11

Competences

Technical Competences of each Specialization

Especifics

  • CTE1 - Capability to model, design, define the architecture, implement, manage, operate, administrate and maintain applications, networks, systems, services and computer contents.
  • CTE6 - Capability to design and evaluate operating systems and servers, and applications and systems based on distributed computing.
  • CTE7 - Capability to understand and to apply advanced knowledge of high performance computing and numerical or computational methods to engineering problems.

Generic Technical Competences

Generic

  • CG1 - Capability to plan, calculate and design products, processes and facilities in all areas of Computer Science.
  • CG3 - Capability to lead, plan and supervise multidisciplinary teams.
  • CG4 - Capacity for mathematical modeling, calculation and simulation in technology and engineering companies centers, particularly in research, development and innovation tasks in all areas related to Informatics Engineering.
  • CG6 - Capacity for general management, technical management and research projects management, development and innovation in companies and technology centers in the area of Computer Science.
  • CG8 - Capability to apply the acquired knowledge and to solve problems in new or unfamiliar environments inside broad and multidisciplinary contexts, being able to integrate this knowledge.

Transversal Competences

Appropiate attitude towards work

  • CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.

Objectives

  1. Present the student with new execution environments required to manage the computing resources and simplify the development and integration of the different types of applications and services at nowadays Internet-scale systems.
    Related competences: CTE1, CTE2, CTR6, CTR1,
  2. Collaborate in the design, implementation and presentation of a cloud computing environment that is required for a class project.
    Related competences: CTE1, CTR3, CTR5, CTR6, CB7, CG4, CG5, CTR1,
  3. Find and understand useful information to create innovative solutions.
    Related competences: CTE2, CTE3, CB8, CG5,

Contents

  1. Lectures: Cloud Computing fundamentals
    Fundamental concepts: The effect of scale on 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. Laboratory sessions: 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. Assigment: 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 regarding APIs or Software as a Service.

Activities

Activity Evaluation act


Presentation of the subject and Introduction to Cloud Computing and Big Data Analytics


Objectives: 1
Theory
4h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Virtualization


Objectives: 1
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Cloud Computing Architecture


Objectives: 1 3
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Best Practices for creating SaaS


Objectives: 2 3
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Cloud Security


Objectives: 1 3
Theory
4h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Shared Knowledge on current trending topics


Objectives: 1 3
Theory
4h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Collaborative class project


Objectives: 2
Theory
5h
Problems
0h
Laboratory
11h
Guided learning
0h
Autonomous learning
36h

Lab 1: Basic knowledge toolbox


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 2: Doors in the cloud


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 3: Basic use of the cloud


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 4: Use of services programmatically through their API


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 5: Deploy a custom web app using additional cloud services


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 6: Run a custom web app in the cloud


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 7: Continuous Integration, Continuous Delivery, and Observability


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Lab 8: Serverless applications


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
2h

Teaching methodology

Lectures, reading, and discussion of technical and research papers, Presentation of topics (and papers) by students. Laboratory sessions and a practical class project.

Students are required to bring their laptops to carry out the laboratory sessions and the practical class project.

Students are responsible for their Amazon Web Services (AWS) account, which serves as their cloud computing service provider.

Evaluation methodology

Students will be evaluated on their participation in class, laboratory sessions, class attendance, reading and presenting reports and papers and assignments on specific topics.

The final grade for the course is the weighted average of the grades for the following components obtained in each part of the course:
· Lab sessions: 40%
· Papers Reading/Presentation and homework: 10%
· Course Projects: 30%
· Final exam: 20%

In order to be able to publicly defend the course project, students must have attended at least 70% of the classes and teams must have delivered on time the activities that have been planned during the course. The course project is the result of teamwork, which will be reflected in the grade given to the group as a whole. Each member of the group will be responsible for part of the project and might be graded individually on his or her contribution.

Bibliography

Basic:

Previous capacities

General knowledge of:
- TCP/IP networking
- Operating Systems basic administration and use of the operating system from the programs
- Software development

Basic knowledge of:
- Unix command line.
- Python programming language.
- Git version control system.

Warning. Students are supposed to have the above background before starting the laboratory sessions. Complimentary fast-paced materials will be provided before class to help students meet the above requirements.