IKT433 Distributed and Big Data Systems

The course is connected to the following study programs

• Master's Programme in Information and Communication Technology
• Artificial Intelligence, 5-year master programme

Teaching language

English

Course contents

The course covers the following distributed computing topics:

Design goals: transparency, openness, scalability.

Communication: remote procedure call, remote object invocation, message-oriented communication.

Processes: threads, clients, servers, code migration, software agents.
Naming: naming objects, locating Mobile Objects, Synchronization.
Consistency & replication. Fault tolerance. Security. Component Architectures. Distributed object-based systems. Distributed coordination based systems.
Big data: Data partitioning, Multilevel Techniques.

Learning outcomes

On successful completion of the course, the student should:

- know the main classes of distributed computing approaches, with in-depth knowledge in selected techniques from each class

- have knowledge of cloud computing, benefits, challenges, deployment and communication

- have acquired competence in big data analysis, decomposition, computing and multilevel search

- be able to cast traditional problems in a distributed computing perspective

- be able to analyze, implement and evaluate distributed computing solutions

- have obtained skills in applying distributed computing in new domains

- understand how big data infrastructures rely on distributed computing principles

- have acquired competence in extracting and presenting knowledge from the distributed computing research literature

Examination requirements

Students must pass the compulsory assignments in order to take the examination. Information about compulsory assignments will be given in Canvas at the start of the course.

Teaching methods

Lectures, compulsory exercises, and self-study. The work load for the average student is approximately 200 hours.

Evaluation

The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.

Offered as Single Standing Module

Yes. Subject to availability or capacity.

Assessment methods and criteria

Written examination, 4 hours. Graded assessment.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course	Reduction of Credits
IKT414 – Distributed Computing and Big Data Infrastructure	5