Queuing networks
Course: Systems and methods of decision making
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Queuing networks
        
    
            Code
        
        
            ДВВ.04
        
    
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2021/2022
        
    
            Semester/trimester when the component is delivered
        
        
            3 Semester
        
    
            Number of ECTS credits allocated
        
        
            3
        
    
            Learning outcomes
        
        
            Know and understand the main sections and tasks of queuing networks, principles of analysis and modeling of queuing networks. Be able to use stochastic networks for mathematical modeling of real telephone and computer networks, development of nuclear physics and pharmacokinetics.
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            Know: basics of mathematical analysis, algebra, probability theory, mathematical statistics. Be able to: formalize tasks and compile algorithms for the implementation of tasks. Have basic skills: working with stochastic objects.
        
    
            Course content
        
        
            Gaining students basic knowledge of methods of accurate and approximate calculation of stochastic networks, solving optimization problems, skills to use stochastic networks for mathematical modeling of real telephone and computer networks, development of nuclear physics and pharmacokinetics.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Anisimov VV, Lebedev EA Stochastic service networks. Markov models: Textbook. allowance. - К .: Либідь, 1992. - 206 с.
2. Computer networks / Edited by Academician VM Glushkova. - М .: Связь, 1977. - 279 с.
3. Lebedev EA, Makushenko IA Risk Optimization for Multichannel Stochastic Networks: A Tutorial. - К .: НБУВ, 2007. - 65 с.
5. Lebedev EA, Makushenko IA Optimal external load distribution for multichannel stochastic networks: A textbook. - К .: НБУВ, 2012. - 90 с.
        
    
            Planned learning activities and teaching methods
        
        
            Lecture, test work, independent work.
        
    
            Assessment methods and criteria
        
        
            Test work, exam.
        
    
            Language of instruction
        
        
            ukrainian
        
    Lecturers
This discipline is taught by the following teachers
                    Igor
                    Anatoliyovych
                    Makushenko
                
                
                    Applied Statistics 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
                        Applied Statistics
                    
                    
                        Faculty of Computer Science and Cybernetics