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Course Detail

Course Name Random Processes And Queueing Models
Course Code 25MA731
Program M.Tech. Wireless Networks & Applications (Specialising in IoT, AI, 5G, Blockchain) (For Working Professionals & Regular Students)
Credits 3
Campus Amritapuri

Syllabus

Syllabus

Random Processes: Introduction, Classification of random processes, Poisson Process, renewal processes, Markov Process, Markov Chains, Transition Probabilities, classification of states of Markov chains, Chapman-Kolmogorov Equations, steady state probabilities, continuous time Markov chains and birth and death processes and analysis of time series.Introduction to Regeneration process. 

Queuing Models: Characteristics of Queuing Systems, Steady state solution of M/M/1 and M/M/C queuing models with Finite and Infinite Capacities, Stationary behavior of M/G/1. Queuing networks, G/G/1.  

Objectives and Outcomes

Course Outcomes (CO) 

CO1: 

Elucidatethe power of stochastic/Random processes and their range of applications 

CO2: 

Demonstrate essential stochastic modelling tools including Markov chains and queuing theory 

CO3: 

Demonstrate the ability to formulate continuous-time Markov chain models for relevant

 practical systems 

CO4: 

Rigorous understanding of the?theoretical background of queueing systems 

CO5: 

Understand and?compute?quantitative metrics of performance for queueing systems. 

CO6: 

Apply and extend queueing models to analyze real world systems. 

Text Books / References

  1. Sheldon M Ross, “Stochastic Processes”, Second Edition, Wiley & Sons Inc, 1996.  
  2. Gross D. and Harris C. M, John,“Fundamentals of Queuing Theory”, Third Edition, Wiley & Sons Inc, 2004.  
  3. J. Medhi, “Stochastic Models in Queuing Theory”, Second Edition, Academic Press, Elsevier, 2003.  

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