Probability And Queueing Theory Balaji.epub
Probability And Queueing Theory Balaji.epub
Probability and queueing theory are two branches of mathematics that deal with random phenomena and their applications. Probability theory studies the likelihood of events and outcomes, while queueing theory models the behavior of systems that involve waiting lines, service processes, and resource allocation. Both fields have many practical uses in engineering, computer science, operations research, and other disciplines.
One of the popular books on probability and queueing theory is written by G.Balaji, a professor of mathematics at Anna University, Chennai. The book is titled "Probability and Queueing Theory" and is published by G.Balaji Publishers. The book covers the syllabus of MA6453 for CSE (IV Semester) and MA8402 for CSE (II Year IV Semester) as per Anna University 2013 and 2017 Regulations respectively. The book also provides free Q&A 18th Edition for students to practice and prepare for exams.
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The book is available in both print and digital formats. The print version can be ordered from various online platforms such as [BooksDelivery], [Mybooksfactory], or directly from the publisher's website. The digital version can be downloaded as an epub file from various sources such as [Harvard Environment] or other file-sharing websites. The epub file can be read on any device that supports the format, such as e-readers, tablets, smartphones, or computers.
The book is divided into eight chapters, each covering a different topic in probability and queueing theory. The chapters are as follows:
Chapter 1: Probability - This chapter introduces the basic concepts and axioms of probability, conditional probability, Bayes' theorem, random variables, probability distributions, moments, moment generating functions, characteristic functions, and Chebyshev's inequality.
Chapter 2: Two-Dimensional Random Variables - This chapter deals with joint probability distributions, marginal and conditional distributions, covariance, correlation coefficient, transformation of random variables, order statistics, and bivariate normal distribution.
Chapter 3: Standard Distributions - This chapter discusses some of the common probability distributions such as binomial, Poisson, geometric, negative binomial, hypergeometric, uniform, exponential, gamma, beta, normal, lognormal, Weibull, Pareto, Cauchy, and Rayleigh distributions.
Chapter 4: Random Processes - This chapter covers the definition and classification of random processes, stationary and ergodic processes, autocorrelation function, power spectral density function, Poisson process, renewal process, Markov process, Markov chain, Chapman-Kolmogorov equation, classification of states and chains, steady-state probabilities, mean recurrence time, absorbing states and chains.
Chapter 5: Queueing Theory - This chapter introduces the basic elements and notation of queueing systems, Kendall's notation for describing queueing models, birth-death process as a special case of Markov process applied to queueing systems.
Chapter 6: Markovian Queues - This chapter analyzes some of the simple queueing models that have Markovian assumptions such as M/M/1 queue (single server with Poisson arrivals and exponential service times), M/M/1/N queue (finite capacity), M/M/C queue (multiple servers), M/M/C/N queue (finite capacity and multiple servers), M/M/ queue (infinite servers), M/Ek/1 queue (single server with Erlang service times), M/G/1 queue (single server with general service times), Pollaczek-Khinchin formula.
Chapter 7: Non-Markovian Queues - This chapter studies some of the more complex queueing models that have non-Markovian assumptions such as M/G/1 with server vacations (server takes breaks after completing service), M/G/1 with feedback (customers may re-enter the system after service), M/D/1 queue (single server with deterministic service times), M/D/C queue (multiple servers with deterministic service times), GI/M/1 queue (general interarrival times and exponential service times), GI/G/1 queue (general interarrival times and general service times).
Chapter 8: Queue Networks - This chapter explores some of the applications of queueing theory to network systems such as Jackson network (a network of interconnected M/M/C queues), Burke's theorem (the output process of an M/M/1 queue is also a Poisson process), open and closed queueing networks, mean value analysis, Gordon-Newell theorem, product form solution.
The book is written in a clear and concise manner, with plenty of examples, exercises, and solved problems. The book also provides references for further reading and research. The book is suitable for undergraduate and postgraduate students of engineering, computer science, mathematics, and statistics who want to learn the fundamentals and applications of probability and queueing theory.