Course detail

Distributed Application Environment

FIT-PDIAcad. year: 2025/2026

Common characteristics of distributed environments. Principles, algorithms, and systems of distributed computing. Types of distributed environments. Design and model of distributed algorithms. Distributed operating and file systems. Cloud Computing. Data-centric computing. Web services. Security in distributed applications.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Ondřej Ryšavý, Ph.D.

Department

Department of Information Systems (UIFS)

Entry knowledge

  • knowledge of programming
  • knowledge discrete mathematics
  • basic knowledge of computer networks

Rules for evaluation and completion of the course

  • Mid-term written examination - 15 points
  • Laboratory exercises - 10 points
  • Evaluated project with the defense - 20 points
  • Final written examination - 55 points

  • Scored laboratory exercises for which at least two terms are listed. The possibility of replacement only in case of objective and proven obstacles in the study.
  • Mid-term exam in the lecture.
  • Evaluated projects with defense in the form of presentation of results.

Aims

The aim is to understand the principles and design of applications for distributed environments, obtain an overview of modern distributed environments, and ability of usage application interfaces for various programming environments.


The students will become familiar with concepts and principles of distributed environments, with the design and implementation of applications for distributed environments and security aspects in distributed environments.

  • A student learns terminology in the domain of DS
  • A student learns to create small projects
  • A student learns to present and defend the results of the small project

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kshemkalyani, Singhal: Distributed Computing, Cambridge Press, 2008.

Recommended reading

B. Burns: Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services,  O'Reilly Media, 1st edition, 2018.
S. Saxena, S. Gupta: Real-Time Big Data Analytics,  Packt Publishing, 2016.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NSEC , 0 year of study, winter semester, elective
    specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, compulsory
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, elective
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective
    specialization NISY , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, compulsory
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Radek Hranický, Ph.D.
Ing. Jan Pluskal, Ph.D.
RNDr. Marek Rychlý, Ph.D.
doc. Ing. Ondřej Ryšavý, Ph.D.

Syllabus

  1. Principles and models of distributed computation
  2. Physical and Logical Time 
  3. Global State and Snapshot Algorithms 
  4. Group communication
  5. Authentication in Distributed Systems 
  6. Graph and Routing Algorithms
  7. Algorithms of Leader Election and Mutual Exclusion
  8. Virtualization and Cloud Computing
  9. MapReduce Programming Model and Apache Hadoop 
  10. Principles of Apache Spark
  11. Distributed Stream Processing in Apache Flink
  12. Enterprise Service Bus 
  13. Distributed computing with BOINC

Exercise in computer lab

6 hod., compulsory

Teacher / Lecturer

RNDr. Marek Rychlý, Ph.D.
Ing. Jan Pluskal, Ph.D.

Syllabus

  1. Apache Hadoop/Spark
  2. Windows Azure Applications

Project

20 hod., compulsory

Teacher / Lecturer

Ing. Jan Pluskal, Ph.D.
RNDr. Marek Rychlý, Ph.D.

Syllabus

  • Implementation of a distributed application in the given target environment (Spark, Flink, Azure, Hadoop,...).