Course details

Distributed Application Environment

PDI Acad. year 2022/2023 Winter semester 5 credits

Current academic year

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. Technology JSP, J2EE, JavaBeans, EJB, RPC, XML-RPC, SOAP, IIOP. Web services. Security in distributed applications.

Guarantor

Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS)

Course coordinator

Pluskal Jan, Ing., Ph.D. (DIFS)
Rychlý Marek, RNDr., Ph.D. (DIFS)

Language of instruction

Czech, English

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 6 hrs pc labs
  • 20 hrs projects

Assessment points

  • 55 pts final exam (written part)
  • 15 pts mid-term test (written part)
  • 10 pts labs
  • 20 pts projects

Department

Department of Information Systems (UIFS)

Lecturer

Hranický Radek, Ing., Ph.D. (DIFS)
Jeřábek Kamil, Ing., Ph.D. (DIFS)
Rychlý Marek, RNDr., Ph.D. (DIFS)
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS)

Instructor

Pluskal Jan, Ing., Ph.D. (DIFS)
Rychlý Marek, RNDr., Ph.D. (DIFS)

Subject specific learning outcomes and competences

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

Learning objectives

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.

Why is the course taught

The course acquaints students with current technologies of distributed systems, which will enable them to participate in the development of modern applications for large data processing.

Prerequisite knowledge and skills

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

Study literature

  • 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.

Fundamental literature

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

Syllabus of lectures

  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

Syllabus of computer exercises

  1. Apache Hadoop/Spark
  2. Windows Azure Applications

Syllabus - others, projects and individual work of students

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

Progress assessment

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

Controlled instruction

  • 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.

Exam prerequisites

  • not applicable

Course inclusion in study plans

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