MA 2262 PROBABILITY AND QUEUING THEORY SYLLABUS | ANNA UNIVERSITY BE CSE 4TH SEM SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FOURTH SEMESTER BE COMPUTER SCIENCE AND ENGINEERING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
MA 2262 PROBABILITY AND QUEUEING THEORY L T P C
(Common to CSE & IT) 3 1 0 4
AIM
The probabilistic models are employed in countless applications in all areas of science
and engineering. Queuing theory provides models for a number of situations that arise in
real life. The course aims at providing necessary mathematical support and confidence
to tackle real life problems.
OBJECTIVES
At the end of the course, the students would
34
Have a well – founded knowledge of standard distributions which can describe
real life phenomena.
Acquire skills in handling situations involving more than one random variable and
functions of random variables.
Understand and characterize phenomena which evolve with respect to time in a
probabilistic manner.
Be exposed to basic characteristic features of a queuing system and acquire
skills in analyzing queuing models.
UNIT I RANDOM VARIABLES 9 + 3
Discrete and continuous random variables - Moments - Moment generating functions
and their properties. Binomial, Poisson ,Geometric ,Negative binomial, Uniform,
Exponential, Gamma, and Weibull distributions .
UNIT II TWO DIMENSIONAL RANDOM VARIABLES 9 + 3
Joint distributions - Marginal and conditional distributions – Covariance - Correlation and
regression - Transformation of random variables - Central limit theorem.
UNIT III MARKOV PROCESSES AND MARKOV CHAINS 9 +3
Classification - Stationary process - Markov process - Markov chains - Transition
probabilities - Limiting distributions-Poisson process
UNIT IV QUEUEING THEORY 9 + 3
Markovian models – Birth and Death Queuing models- Steady state results: Single and
multiple server queuing models- queues with finite waiting rooms- Finite source models-
Little’s Formula
UNIT V NON-MARKOVIAN QUEUES AND QUEUE NETWORKS 9 + 3
M/G/1 queue- Pollaczek- Khintchine formula, series queues- open and closed networks
TUTORIAL 15, TOTAL : 60 PERIODS TEXT BOOKS:
1. O.C. Ibe, “Fundamentals of Applied Probability and Random Processes”,
Elsevier, 1st Indian Reprint, 2007 (For units 1, 2 and 3).
2. D. Gross and C.M. Harris, “Fundamentals of Queueing Theory”, Wiley
Student edition, 2004 (For units 4 and 5).
REFERENCES:
1. A.O. Allen, “Probability, Statistics and Queueing Theory with Computer
Applications”, Elsevier, 2nd edition, 2005.
2. H.A. Taha, “Operations Research”, Pearson Education, Asia, 8th edition, 2007.
3. K.S. Trivedi, “Probability and Statistics with Reliability, Queueing and
Computer Science Applications”, John Wiley and Sons, 2nd edition, 2002.
MA 2262 PROBABILITY AND QUEUEING THEORY L T P C
(Common to CSE & IT) 3 1 0 4
AIM
The probabilistic models are employed in countless applications in all areas of science
and engineering. Queuing theory provides models for a number of situations that arise in
real life. The course aims at providing necessary mathematical support and confidence
to tackle real life problems.
OBJECTIVES
At the end of the course, the students would
34
Have a well – founded knowledge of standard distributions which can describe
real life phenomena.
Acquire skills in handling situations involving more than one random variable and
functions of random variables.
Understand and characterize phenomena which evolve with respect to time in a
probabilistic manner.
Be exposed to basic characteristic features of a queuing system and acquire
skills in analyzing queuing models.
UNIT I RANDOM VARIABLES 9 + 3
Discrete and continuous random variables - Moments - Moment generating functions
and their properties. Binomial, Poisson ,Geometric ,Negative binomial, Uniform,
Exponential, Gamma, and Weibull distributions .
UNIT II TWO DIMENSIONAL RANDOM VARIABLES 9 + 3
Joint distributions - Marginal and conditional distributions – Covariance - Correlation and
regression - Transformation of random variables - Central limit theorem.
UNIT III MARKOV PROCESSES AND MARKOV CHAINS 9 +3
Classification - Stationary process - Markov process - Markov chains - Transition
probabilities - Limiting distributions-Poisson process
UNIT IV QUEUEING THEORY 9 + 3
Markovian models – Birth and Death Queuing models- Steady state results: Single and
multiple server queuing models- queues with finite waiting rooms- Finite source models-
Little’s Formula
UNIT V NON-MARKOVIAN QUEUES AND QUEUE NETWORKS 9 + 3
M/G/1 queue- Pollaczek- Khintchine formula, series queues- open and closed networks
TUTORIAL 15, TOTAL : 60 PERIODS TEXT BOOKS:
1. O.C. Ibe, “Fundamentals of Applied Probability and Random Processes”,
Elsevier, 1st Indian Reprint, 2007 (For units 1, 2 and 3).
2. D. Gross and C.M. Harris, “Fundamentals of Queueing Theory”, Wiley
Student edition, 2004 (For units 4 and 5).
REFERENCES:
1. A.O. Allen, “Probability, Statistics and Queueing Theory with Computer
Applications”, Elsevier, 2nd edition, 2005.
2. H.A. Taha, “Operations Research”, Pearson Education, Asia, 8th edition, 2007.
3. K.S. Trivedi, “Probability and Statistics with Reliability, Queueing and
Computer Science Applications”, John Wiley and Sons, 2nd edition, 2002.
how to pass pqt in one day
ReplyDelete