Distributed Systems
Code: 44212 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Modelización para la Ciencia y la Ingeniería / Modelling for Science and Engineering | OP | 1 |
Contact
- Name:
- Alvaro Wong Gonzalez
- Email:
- alvaro.wong@uab.cat
Teachers
- Daniel Franco Puntes
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is recommended to have a basic knowledge of programming languages like Python and basic skills of any Linux distribution.
Objectives and Contextualisation
The objectives of the module:
-Solve data analysis problems with open source tools
-Understand tool data management limitations and learn criteria to select suitable tools for a specific problem
-Learn data query methodologies related to each technology
-Use Cloud Computing providers to solve data analysis problems
-Apply a data analysis methodology to solve practical problems
By the end of the lectures and practical labs students should have enough knowledge to understand the requirements of typical large data analysis problems in industrial and academic contexts. They should be able to pick some combination of tools and design a solution for a given large data analysis problem. This subject is oriented to develop data problem solving skills. Languages, tools and techniques are described in a data analysis context and students will solve a list of data problems applying the technology described at every chapter.
Learning Outcomes
- CA24 (Competence) Apply the computational tools for large database analysis to solve problems in the industrial or research field.
- CA25 (Competence) Integrate computational tools for large database analysis in multidisciplinary work environments.
- CA26 (Competence) Work in multidisciplinary teams with the aim of developing projects where large database analysis techniques are applied.
- KA19 (Knowledge) Characterise the structure and performance of the different architectures and structures of database organisation and/or management.
- KA20 (Knowledge) Describe the cloud computing tools used for data analysis, evaluating their advantages and limitations.
- SA24 (Skill) Use specific software to solve data processing problems in distributed systems.
- SA25 (Skill) Develop computer applications aimed at modelling a specific process using distributed systems, also evaluating their computational performance.
- SA26 (Skill) Interpret the reports and results of the analysis of a specific database.
Content
T1: Introduction to Distributed Systems and large data processing systems (2 hours)
T2: Cloud computing (2 hours)
- Introduction to cloud computing
- Data analysis with a cloud computing provider: AWS / Azure
T3: Cluster and supercomputer infraestructures (14 jours)
- Principles of job execution under batch queue systems (SLURM).
- Advanced control of jobs: array jobs, dependencies, process binding, heterogeneous resources (GPUs)
- Creation of large jobs and workflows
- Virtualization and environments
T4: Cloud Networking and Virtual Private Clouds (8 hours)
- Intro to VPC
- Build our VPC and launch a web server tutorial
- VPC Lab
T5: Fault tolerance systems (4 hours)
- Availability zones
- Load balancing and autoscaling tutorial
- ELB Lab
T6: Database Cloud project: relational and DynamoDB implementations (8 hours)
- Intro to RDS and DynamoDB
- Build a database server tutorial
- Distributed database Lab
T7: Serverless services and Lambda (2 hours)
- Intro to Lambda services and serverless computing
- Lambda tutorial
- Lambda Lab
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Laboratory | 24 | 0.96 | CA24, CA25, SA24, CA24 |
| Lectures | 38 | 1.52 | KA19, KA20, SA25, KA19 |
| Type: Autonomous | |||
| Practical exercise development | 62 | 2.48 | CA26, SA26, CA26 |
The development of the subject will be based fundamentally on the concept of "learning by doing". There will be initial theoretical sessions for each topic, in which the teacher will present the key concepts and students will be provided with complementary study materials (books, online teaching resources, articles and other technical documentation). With this information, students will work in practical sessions solving exercises and small projects individually or in groups of two people. Students will prepare written reports on the practical work carried out in each topic.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| ELB Lab | 20% | 6 | 0.24 | CA26, SA24, SA25, SA26 |
| Infrastructure lab | 30% | 6 | 0.24 | CA24, CA26, SA25, SA26 |
| Lambda Lab | 10% | 4 | 0.16 | CA26, KA20, SA25, SA26 |
| RDS Lab | 20% | 6 | 0.24 | CA25, CA26, SA25, SA26 |
| VPC Lab | 20% | 4 | 0.16 | CA26, KA19, SA25, SA26 |
Evaluation will come out from the combination of work developed in the lab sessions and the corresponding reports.
Bibliography
Martin Kleppmann. "Designing Data-Intensive Applications". O'Reilly, 2017.
A. Wittig, M. Wittig. "Amazon Web Services in Action", Manning, 2nd Edition, 2018.
G. Coulouris, J. Dollimore and T. Kinderg, "Distributed Systems. Concepts and design ", Addison-Wesley, 5th edition, 2012.
Bell, Charles; Kindahl, Mats; Thalmann, Lars. "MySQL High Availability". O'Reilly, 2010.
Chang, Fay, et al. "Bigtable: A Distributed Storage System for Structured Data." OSDI, 2006
Dewitt, David, and Jim Gray. "Parallel Database Systems: The Future of High Performance Database Processing." Communications of the ACM 35, no. 6 (1992): 85-98
Schwartz, Baron; Zaitsev, Peter; Tkachenko, Vadim; Zawodny, Jeremy D.; Lentz, Arjen; Balling, Derek J. "High Performance MySQL", O'Reilly, 2008.
Seyed M. M. "Saied" Tahaghoghi and Hugh E. Williams. Learning MySQL. O’Reilly, 2006
Nathan Haines. “Beginning Ubuntu for Windows and Mac Users”. Apress 2015. Available as electronic resource at UAB library
William E. Shotts. “The Linux Command Line”. Second Internet Edition. 2013. http://linuxcommand.org/tlcl.php
Dan C. Marinescu. “Cloud Computing. Theory and Practice”. Morgan-Kaufmann. 2018.
R. Buyya, R. N. Calheiros, A. V. Dastjerdi. “Big data. Principles and paradigms”. Morgan-Kaufmann. 2016.
Software
In the subject, we are going to use the last version of the following software platforms and tools
-Ubuntu Linux
-SLURM
-Linux development environment
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLABm) Practical laboratories (master) | 1 | English | first semester | afternoon |
| (TEm) Theory (master) | 1 | English | first semester | afternoon |