| Study programme competencies |
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Code
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Study programme competences
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| A1 |
CE1 - Define, evaluate and select the most appropriate architecture and software to solve a problem |
| A6 |
CE6 - Know the available tools for the distributed systems computing |
| B2 |
CB7 - The students have to know how to apply the acquired knowledge and their capacity to solve problems in new or hardly explored environment inside wider contexts (or multidiscipinary) related to its area of development |
| B5 |
CB10 - The students have to possess learning skills that allows them to continue to study in a mainly self-driven or autonomous manner |
| B6 |
CG1 - Be able to search and select useful information to solve complex problems, using the bibliographic sources of the field |
| C1 |
CT1 - Use the basic technologies of the information and computing technology field required for the professional development and the long-life learning |
| Learning aims |
| Learning outcomes |
Study programme competences |
| The student will know the basics of cloud computing and service virtualization. |
AJ6
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| The student will know and learn to use the basic services provided by one of the main Cloud public providers. |
AJ1
AJ6
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CJ1
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| The student will know and know how to apply the main paradigms of distributed programming used in Cloud computing. |
AJ1
AJ6
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BJ2
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CJ1
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| The student will know and learn to use the services and resources available in the cloud to prepare and execute applications in the field of high performance computing. |
AJ6
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CJ1
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| The student will acquire the necessary skills for the search, selection and management of resources (bibliography, software, etc.) related to Cloud computing in the field of high performance computing. |
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BJ5
BJ6
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| Contents |
| Topic |
Sub-topic |
| Introduction to Cloud Computing |
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| Cloud Computing services: virtual clusters |
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| Distributed processing models and frameworks |
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| Services for distributed processing in the cloud |
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| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Guest lecture / keynote speech |
A1 A6 |
24 |
0 |
24 |
| Laboratory practice |
A1 A6 B2 B5 B6 C1 |
12 |
63 |
75 |
| Supervised projects |
B2 B5 B6 |
0 |
40 |
40 |
| Objective test |
A1 A6 B2 B6 |
2 |
0 |
2 |
| |
| Personalized attention |
|
9 |
0 |
9 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Guest lecture / keynote speech |
In which the content of each topic is exposed. The student will have all the supporting material in advance (notes, slides used by the lecturer, articles, etc.). The lecturer will promote an active attitude, asking questions that will clarify specific aspects and leaving open questions for the student's reflection. |
| Laboratory practice |
The students will resolve diverse problems which allow them to practice the topics introduced in the keynote lectures. |
| Supervised projects |
The subject of an individual assignment will be agreed with the teacher and the student will elaborate it more deeply in an autonomous way. |
| Objective test |
At the end of the semester there will be an exam on the contents of the subject. In this exam the topics discussed in the theoretical and practical classes will be evaluated. |
| Personalized attention |
|
Methodologies
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| Supervised projects |
| Laboratory practice |
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| Description |
The personalized attention during the laboratory practices will serve to guide and check the students' work following to the indications they were given.
To carry out the supervised assignments, students will be given the necessary initial indications and bibliographic references for consultation. During the elaboration, their progress will be monitored to offer additional guidelines to ensure the quality of the result according to predefined criteria.
Every teacher will provide a tutorial schedule to resolve students' questions related to the topics of the subject. Students will be encouraged to take advantage of the tutorial sessions as a fundamental part of their learning process. |
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| Assessment |
|
Methodologies
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Competencies |
Description
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Qualification
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| Objective test |
A1 A6 B2 B6 |
A proba poderá conter preguntas tipo test, de resposta breve ou resolución de xercicios relacionadas coa temática tratada nas sesións maxistrais e nas prácticas de laboratorio. |
40 |
| Supervised projects |
B2 B5 B6 |
Os traballos tutelados serán sobre algún tema a convenir entre o alumno e o profesor. Valorarase o cumprimento das especificacións, a orixinalidade, a contribución personal, a metodoloxía e rigorosidade e a presentación de resultados. |
20 |
| Laboratory practice |
A1 A6 B2 B5 B6 C1 |
Valorarase o grao de cumprimento das especificacións, a metodoloxía e rigorosidade e a presentación de resultados. |
40 |
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Assessment comments |
In order to pass the subject, a minimum score of 5 out of 10 must be obtained in the practices and supervised assignment, and 5 out of 10 in the exam. Furthermore, the total subject score must be of 5 or higher. Notes of students that fail the subject are not kept for the following course. Second opportunity (July) and extraordinary The evaluation will be the same as in the first opportunity. Students will have a second deadline before the final exam to submit failed practical assignments. Condition to be considered "Absent" Do not present any assignment and do not take part in the exam. Fraud The fraud regulation of the UDC will be applied in case fraud was detected in any assignment or in the exam. |
| Sources of information |
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Basic
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- Erl T., Puttini R. and Mahmood Z. Cloud Computing, Concepts, Technology & Architecture (2013). Ed. Prentice-Hall.
- White, T. Hadoop: The Definitive Guide, Storage and Analysis at Internet Scale, 4ª edición (2015). O'Reilly Media. - B. Chambers, M. Zaharia, "Spark: The Definitive Guide", O'Reilly, 2018 |
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Complementary
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- Foster, I. and Gannon, D.B. Cloud Computing for Science and Engineering (2017). The MIT Press.
- Zaharia, M., Karau, H., Konwinski, A. y Patrick Wendell. Learning Spark: Lightning-Fast Big Data Analysis (2015), O'Reilly Media.
- Karau, H., Warren, R,. High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark, (2017). O'Reilly Media. |
| Recommendations |
| Subjects that it is recommended to have taken before |
| Parallel Programming/614473102 |
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| Subjects that are recommended to be taken simultaneously |
| High Performance Infrastructures/614473104 |
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| Subjects that continue the syllabus |
| Data Analytics with HPC/614473108 |
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| Other comments |
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Considering the strong interrelation between the theoretical and practical contents of the subject and the progressive introduction of new concepts closely related to each other, it is advisable a weekly review to make the most of the subject. An intensive use of online communication tools will be encouraged: videoconference, e-mail, chat, etc. |
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