Introduction to Queueing Theory and Stochastic Teletraffic Models

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1 Introduction to Queueing Theory and Stochastic Teletraffic Models Moshe Zukerman EE Department, City University of Hong Kong Copyright M. Zukerman c Preface The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modelling, resource provisioning and traffic management. These study areas are often collectively called teletraffic. This book assumes prior knowledge of a programming language, mathematics normally taught in an electrical engineering course. The book aims to enhance intuitive and physical understanding of the theoretical concepts it introduces. The famous mathematician Pierre-Simon Laplace is quoted to say that Probability is common sense reduced to calculation [14]; as the content of this book falls under the field of applied probability, Laplace s quote very much applies. Accordingly, the book aims to link intuition and common sense to the mathematical models and techniques it uses. A unique feature of this book is the considerable attention given to guided projects involving computer simulations and analyzes. By successfully completing the programming assignments, students learn to simulate and analyze stochastic models, such as queueing systems and networks, and by interpreting the results, they gain insight into the queueing performance effects and principles of telecommunications systems modelling. Although the book, at times, provides intuitive explanations, it still presents the important concepts and ideas required for the understanding of teletraffic, queueing theory fundamentals and related queueing behavior of telecommunications networks and systems. These concepts and ideas form a strong base for the more mathematically inclined students who can follow up with the extensive literature on probability models and queueing theory. A small sample of it is listed at the end of this book. The first two chapters provide a revision of probability and stochastic processes topics relevant to the queueing and teletraffic models of this book. These two chapters provide a summary of the key topics with relevant homework assignments that are especially tailored for understanding the queueing and teletraffic models discussed in later chapters. The content of these chapters is mainly based on [14, 25, 73, 78, 79, 80]. Students are encouraged to study also

2 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 2 the original textbooks that include far more explanations, illustrations, discussions, examples and homework assignments. Chapter 3 discusses general queueing notation and concepts and it should be studied well. Chapter 4 aims to assist the student to perform simulations of queueing systems. Simulations are useful and important in the many cases where exact analytical results are not available. An important learning objective of this book is to train students to perform queueing simulations. Chapter 5 provides analyses of deterministic queues. Many queueing theory books tend to exclude deterministic queues; however, the study of such queues is useful for beginners in that it helps them better understand non-deterministic queueing models. Chapters 6 14 provide analyses of a wide range of queueing and teletraffic models most of which fall under the category of continuous-time Markov-chain processes. Chapter 15 provides an example of a discrete-time queue that is modelled as a discrete-time Markov-chain. In Chapters 16 and 17, various aspects of a single server queue with Poisson arrivals and general service times are studied, mainly focussing on mean value results as in [13]. Then, in Chapter 18, some selected results of a single server queue with a general arrival process and general service times are provided. Next, in Chapter 19, we extend our discussion to queueing networks. Finally, in Chapter 20, stochastic processes that have been used as traffic models are discussed with special focus on their characteristics that affect queueing performance. Throughout the book there is an emphasis on linking the theory with telecommunications applications as demonstrated by the following examples. Section 1.19 describes how properties of Gaussian distribution can be applied to link dimensioning. Section 6.6 shows, in the context of an M/M/1 queueing model, how optimally to set a link service rate such that delay requirements are met and how the level of multiplexing affects the spare capacity required to meet such delay requirement. An application of M/M/ queueing model to a multiple access performance problem [13] is discussed in Section 7.6. In Sections 8.8 and 9.5, discussions on dimensioning and related utilization issues of a multi-channel system are presented. Especially important is the emphasis on the insensitivity property of models such as M/M/, M/M/k/k, processor sharing and multi-service that lead to practical and robust approximations as described in Sections 7, 8, 13, and 14. Section 19.4 guides the reader to simulate a mobile cellular network. Section 20.6 describes a traffic model applicable to the Internet. Last but not least, the author wishes to thank all the students and colleagues that provided comments and questions that helped developing and editing the manuscript over the years.

3 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 3 Contents 1 Revision of Relevant Probability Topics Events, Sample Space, and Random Variables Probability, Conditional Probability and Independence Probability and Distribution Functions Joint Distribution Functions Conditional Probability for Random Variables Independence between Random Variables Convolution Selected Discrete Random Variables Non-parametric Bernoulli Geometric Binomial Poisson Pascal Discrete Uniform Continuous Random Variables and their Distributions Selected Continuous Random Variables Uniform Exponential Relationship between Exponential and Geometric Random Variables Hyper-Exponential Erlang Hypo-Exponential Gaussian Pareto Moments and Variance Mean (or Expectation) Moments Variance Conditional Mean and the Law of Iterated Expectation Conditional Variance and the Law of Total Variance Mean and Variance of Specific Random Variables Sample Mean and Sample Variance Covariance and Correlation Transforms Z-transform Laplace Transform Multivariate Random Variables and Transform Probability Inequalities and Their Dimensioning Applications Limit Theorems Link Dimensioning Case 1: Homogeneous Individual Sources Case 2: Non-homogeneous Individual Sources Case 3: Capacity Dimensioning for a Community

4 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 4 2 Relevant Background in Stochastic Processes General Concepts Two Orderly and Memoryless Point Processes Bernoulli Process Poisson Process Markov Modulated Poisson Process Discrete-time Markov-chains Definitions and Preliminaries Transition Probability Matrix Chapman-Kolmogorov Equation Marginal Probabilities Properties and Classification of States Steady-State Probabilities Birth and Death Process Reversibility Multi-Dimensional Markov-chains Continuous Time Markov-chains Definitions and Preliminaries Birth and Death Process First Passage Time Transition Probability Function Steady-State Probabilities Multi-Dimensional Continuous Time Markov-chains The Curse of Dimensionality Simulations Reversibility Renewal Process General Queueing and Teletraffic Concepts Notation Utilization Little s Formula Traffic Work Conservation PASTA Bit-rate Versus Service Rate Queueing Models Simulations Confidence Intervals Simulation of a G/G/1 Queue Deterministic Queues D/D/ D/D/k D/D/k/k The D/D/k/k process and its cycles

5 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman Blocking probability, mean queue-size and utilization Proportion of time spent in each state Summary of Results M/M/ Steady-State Queue Size Probabilities State Transition Diagram of M/M/ Delay Statistics Mean Delay of Delayed Customers Using Z-Transform Multiplexing Dimensioning Based on Delay Distribution The Departure Process Mean Busy Period and First Passage Time A Markov-chain Simulation of M/M/ M/M/ Offered and Carried Traffic Steady-State Equations Solving the Steady-State Equations State Transition Diagram of M/M/ Insensitivity Applications A multi-access model Birth rate evaluation M/M/k/k and Extensions M/M/k/k: Offered, Carried and Overflow Traffic The Steady-State Equations and Their Solution Recursion and Jagerman Formula The Special Case: M/M/1/ Lower bound of E k (A) Monotonicity of E k (A) The Limit k with A/k Fixed M/M/k/k: Dimensioning and Utilization M/M/k/k under Critical Loading Insensitivity and Many Classes of Customers A Markov-chain Simulation of M/M/k/k M/M/k/k with Preemptive Priorities Overflow Traffic of M/M/k/k Multi-server Loss Systems with Non-Poisson Input M/M/k Steady-State Equations and Their Solution Erlang C Formula Mean Queue Size, Delay, Waiting Time and Delay Factor Mean Delay of Delayed Customers Dimensioning

6 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman Utilization Engset Loss Formula Steady-State Equations and Their Solution Blocking Probability Obtaining the Blocking Probability by a Recursion Insensitivity Load Classifications and Definitions The Small Idle Period Limit The Many Sources Limit Obtaining the Blocking Probability by Successive Iterations State Dependent SSQ Queueing Models with Finite Buffers D/D/1/N SLB/D/1/N M/M/1/N M/M/k/N MMPP(2)/M/1/N M/E m /1/N Saturated Queues Processor Sharing The M/M/1-PS queue Insensitivity Multi-service Loss Model Model Description Attributes of the Multi-service Loss Model A Simple Example with I = 2 and k = Other Reversibility Criteria for Markov Chains Computation A General Treatment Infinite number of servers Finite Number of Servers Critical Loading Discrete-Time Queue M/G/ Pollaczek Khintchine Formula: Residual Service Approach [13] Pollaczek Khintchine Formula: by Kendall s Recursion [51] Special Cases: M/M/1 and M/D/ Busy Period M/G/1 with non-fifo disciplines M/G/1-LIFO

7 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman M/G/1 with m priority classes Nonpreemptive Preemptive Resume Queues with General Input Reich s Formula Queue Size Versus Virtual Waiting Time G/GI/1 Queue and Its G/GI/1/k Equivalent Queueing Networks Jackson Networks Computation of Blocking Probability in Circuit Switched Networks by Erlang Fixed-Point Approximation Parameter Conversion in Optical Circuit Switched Networks A Markov-chain Simulation of a Mobile Cellular Network Stochastic Processes as Traffic Models Parameter Fitting Poisson Process Markov Modulated Poisson Process (MMPP) Autoregressive Gaussian Process Exponential Autoregressive (1) Process Poisson Pareto Burst Process

8 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 8 1 Revision of Relevant Probability Topics Probability theory provides the foundation for queueing theory and stochastic teletraffic models, therefore it is important that the student masters the probability concepts required for the material that follows. We aim to provide in this chapter sufficient coverage for readers that have some probability background. Although the cover here is comprehensive in the sense that it discusses all the probability concepts and techniques used in later chapters, it does not include the many examples and exercises that are normally included in a probability textbook to help readers grasp the material better. Therefore, readers without prior probability background may be aided by additional probability texts, such as [14] and [79]. 1.1 Events, Sample Space, and Random Variables Consider an experiment with an uncertain outcome. The term experiment refers to any uncertain scenario, such as tomorrow s weather, tomorrow s share price of a certain company, or the result of flipping a coin. The sample space is a set of all possible outcomes of an experiment. An event is a subset of the sample space. Consider, for example, an experiment which consists of tossing a die. The sample space is {1, 2, 3, 4, 5, 6}, and an event could be the set {2, 3}, or {6}, or the empty set {} or even the entire sample space {1, 2, 3, 4, 5, 6}. Events are called mutually exclusive if their intersection is the empty set. A set of events is said to be exhaustive if its union is equal to the sample space. A random variable is a real valued function defined on the sample space. This definition appears somewhat contradictory to the wording random variable as a random variable is not at all random, because it is actually a deterministic function which assigns a real valued number to each possible outcome of an experiment. It is the outcome of the experiment that is random and therefore the name: random variable. If we consider the flipping a coin experiment, the possible outcomes are Head (H) and Tail (T), hence the sample space is {H, T}, and a random variable X could assign X = 1 for the outcome H, and X = 0 for the outcome T. If X is a random variable then Y = g(x) for some function g( ) is also a random variable. In particular, some functions of interest are Y = cx for some constant c and Y = X n for some integer n. If X 1, X 2, X 3,..., X n is a sequence of random variables, then Y = n i=1 X i is also a random variable. Homework 1.1 Consider the experiment to be tossing a coin. What is the Sample Space? What are the events associated with this Sample Space? Guide Notice that although the sample space includes only the outcome of the experiments which are Head (H) and Tail (T), the events associated with this samples space includes also the empty

9 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 9 set which in this case is the event {H T } and the entire sample space which in this case is the event {H T }. 1.2 Probability, Conditional Probability and Independence Consider a sample space S. Let A be a subset of S, the probability of A is the function on S and all its subsets, denoted P(A), that satisfies the following three axioms: 1. 0 P (A) 1 2. P (S) = 1 3. The probability of the union of mutually exclusive events is equal to the sum of the probabilities of these events. Normally, higher probability signifies higher likelihood of occurrence. In particular, if we conduct a very large number of experiments, and we generate the histogram by measuring how many times each of the possible occurrences actually occurred. Then we normalize the histogram by dividing all its values by the total number of experiments to obtain the relative frequencies. These measurable relative frequencies can provide intuitive interpretation to the theoretical concept of probability. Accordingly, the term limiting relative frequency is often used as interpretation of probability. We use the notation P (A B) for the conditional probability of A given B, which is the probability of the event A given that we know that event B has occurred. If we know that B has occurred, it is our new sample space, and for A to occur, the relevant experiments outcomes must be in A B, hence the new probability of A, namely the probability P (A B), is the ratio between the probability of A B and the probability of B. Accordingly, P (A B) = P (A B). (1) P (B) Since the event {A B} is equal to the event {B A}, we have that so by (1) we obtain P (A B) = P (B A) = P (B A)P (A), P (A B) = P (B A)P (A). (2) P (B) Eq. (2) is useful to obtain conditional probability of one event (A) given another (B) when P (B A) is known or easier to obtain then P (A B). Remark: The intersection of A and B is also denoted by A, B or AB in addition to A B. If events A and B are independent, which means that if one of them occurs, the probability of the other to occur is not affected, then P (A B) = P (A) (3)

10 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 10 and hence, by Eq. (1), if A and B are independent then, P (A B) = P (A)P (B). (4) Let B 1, B 2, B 3,..., B n be a sequence of mutually exclusive and exhaustive events in S, and let A be another event in S. Then, A = n (A B i ) (5) i=1 and since the B i s are mutually exclusive, the events A B i s are also mutually exclusive. Hence, n P (A) = P (A B i ). (6) Thus, by Eq. (1), P (A) = i=1 n P (A B i ) P (B i ). (7) i=1 The latter is a very useful formula for deriving probability of a given event by conditioning and unconditioning on a set of mutually exclusive and exhaustive events. It is called the Law of Total Probability. Therefore, by Eqs. (7) and (1) (again), we obtain the following formula for conditional probability between two events: P (B 1 A) = P (A B 1 )P (B 1 ) n i=1 P (A B i) P (B i ). (8) The latter is known as Bayes theorem (or Bayes law or Bayes rule). Note that Eq. (1) is also referred to as Bayes theorem. 1.3 Probability and Distribution Functions Random variables are related to events. When we say that random variable X takes value x, this means that x represents a certain outcome of an experiment which is an event, so {X = x} is an event. Therefore, we may assign probabilities to all possible values of the random variable. This function denoted P X (x) = P (X = x) will henceforth be called probability function. Other names used in the literature for a probability function include probability distribution function, probability distribution, or simply distribution. Because probability theory is used in many applications, in many cases, there are many alternative terms to describe the same thing. It is important that the student is familiar with these alternative terms, so we will use these terms alternately in this book. The cumulative distribution function) of random variable X is defined for all x R (R being the set of all real numbers), is defined as F X (x) = P (X x). (9) Accordingly, the complementary distribution function F X (x) is defined by F X (x) = P (X > x). (10)

11 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 11 Consequently, for any random variable, for every x R, F (x) + F (x) = 1. As the complementary and the cumulative distribution functions as well as the probability function can be obtained from each other, we will use the terms distribution function when we refer to any of these functions without being specific. We have already mentioned that if X is a random variable, then Y = g(x) is also a random variable. In this case, if P X (x) is the probability function of X then the probability function of Y is P Y (y) = P X (x). (11) x:g(x)=y 1.4 Joint Distribution Functions In some cases, we are interested in the probability that two or more random variables are within a certain range. For this purpose, we define, the joint distribution function for n random variables X 1, X 2,..., X n, as follows: F X1, X 2,...,X n (x 1, x 2,..., x n ) = P (X 1 x 1, X 2 x 2,..., X n x n ). (12) Having the joint distribution function, we can obtain the distribution function of a single random variable, say, X 1, as F X1 (x 1 ) = F X1, X 2,..., X n (x 1,,..., ). (13) A random variable is called discrete if it takes at most a countable number of possible values. On the other hand, a continuous random variable takes an uncountable number of possible values. When the random variables X 1, X 2,..., X n are discrete, we can use their joint probability function which is defined by P X1, X 2,..., X n (x 1, x 2,..., x n ) = P (X 1 = x 1, X 2 = x 2,..., X n = x n ). (14) The probability function of a single random variable can then be obtained by P X1 (x 1 ) = P X1, X 2,..., X n (x 1, x 2,..., x n ). (15) x 2 x n In this section and in sections 1.5, 1.6, 1.7, when we mention random variables or their distribution functions, we consider them all to be discrete. Then, in Section 1.9, we will introduce the analogous definitions and notation relevant to their continuous counterparts. We have already mention the terms probability function, distribution, probability distribution function and probability distribution. These terms apply to discrete as well as to continuous random variables. There are however additional terms that are used to describe probability function only for discrete random variable they are: probability mass function, and probability mass, and there are equivalent terms used only for continuous random variables they are probability density function, density function and simply density.

12 P Y (y) = x Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman Conditional Probability for Random Variables The conditional probability concept, which we defined for events, can also apply to random variables. Because {X = x}, namely, the random variable X takes value x, is an event, by the definition of conditional probability (1) we have P (X = x Y = y) = P (X = x, Y = y). (16) P (Y = y) Let P X Y (x y) = P (X = x Y = y) be the conditional probability of a discrete random variable X given Y, we obtain by (16) P X Y (x y) = P X,Y (x, y). (17) P Y (y) Noticing that P X,Y (x, y), (18) we obtain by (17) P X Y (x y) = 1. (19) x This means that if we condition on the event {Y = y} for a specific y, the probability function of X given {Y = y} is a legitimate probability function. This is consistent with our discussion above. The event {Y = y} is the new sample space and X has a legitimate distribution function there. By (17) P X,Y (x, y) = P X Y (x y)p Y (y) (20) and by symmetry P X,Y (x, y) = P Y X (y x)p X (x) (21) so the latter and (18) gives P Y (y) = x P X,Y (x, y) = x P Y X (y x)p X (x) (22) which is another version of the Law of Total Probability (7). 1.6 Independence between Random Variables The definition of independence between random variables is very much related to the definition of independence between events because when we say that random variables U and V are independent, it is equivalent to say that the events {U = u} and {V = v} are independent for every u and v. Accordingly, random variables U and V are said to be independent if P U,V (u, v) = P U (u)p V (v) for all u, v. (23) Notice that by (20) and (23), we obtain an equivalent definition of independent random variables U and V which is P U V (u v) = P U (u) (24) which is equivalent to P (A B) = P (A) which we used to define independent events A and B.

13 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman Convolution Consider independent random variables V 1 and V 2 that have probability functions P V1 (v 1 ) and P V2 (v 2 ), respectively, and their sum which is another random variable V = V 1 + V 2. Let us now derive the probability function P V (v) of V. P V (v) = P (V 1 + V 2 = v) = v 1 P (V 1 = v 1, V 2 = v v 1 ) = v 1 P V1 (v 1 )P V2 (v v 1 ). The latter is called the convolution of the probability functions P V1 (v 1 ) and P V2 (v 2 ). Let us now extend the result from two to k random variables. Consider k independent random variables X i, i = 1, 2, 3,..., k. Let P Xi (x i ) be the probability function of X i, for i = 1, 2, 3,..., k, and let Y = k i=1 X i. If k = 3, we first compute the convolution of X 1 and X 2 to obtain the probability function of V = X 1 + X 2 using the above convolution formula and then we use the formula again to obtain the probability function of Y = V +X 3 = X 1 +X 2 +X 3. Therefore, for an arbitrary k, we obtain ( ) k P Y (y) = P X1 (y Σ k i=2x i ) P Xi (x i ). (25) x 2, x 3,..., x k : x 2 +x x k y If all the random variable X i, i = 1, 2, 3,..., k, are independent and identically distributed (IID) random variables, with probability function P X1 (x), then the probability function P Y (y) is called the k-fold convolution of P X1 (x). i=2 Homework 1.2 Consider again the experiment to be tossing a coin. Assume that P (H) = P (T ) = 0.5. Illustrate each of the Probability Axioms for this case. Homework 1.3 Now consider an experiment involving three coin tosses. The outcome of the experiment is now a 3-long string of Heads and Tails. Assume that all coin tosses have probability 0.5, and that the coin tosses are independent events. 1. Write the sample space where each outcome of the experiment is an ordered 3-long string of Heads and Tails. 2. What is the probability of each outcome? 3. Consider the event Find P (A) using the additivity axiom. A = {Exactly one head occurs}.

14 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 14 Partial Answer: P (A) = 1/8 + 1/8 + 1/8 = 3/8. Homework 1.4 Now consider again three coin tosses. Find the probability P (A B) where A and B are the events: A = more that one head came up B = 1st toss is a head. Guide: P (B) = 4/8; P (A B) = 3/8; P (A B) = (3/8)/(4/8) = 3/4. Homework 1.5 Consider a medical test for a certain disease. The medical test detects the disease with probability 0.99 and fails to detect the disease with probability If the disease is not present, the test indicates that it is present with probability 0.02 and that it is not present with probability Consider two cases: Case a: The test is done on a randomly chosen person from the population where the occurrence of the disease is 1/ Case b: The test is done on patients that are referred by a doctor that have a prior probability (before they do the test) of 0.3 to have the disease. Find the probability of a person to have the disease if the test shows positive outcome in each of these cases. Guide: A = person has the disease. B = test is positive. Ā = person does not have the disease. B = test is negative. We need to find P (A B). Case a: We know: P (A) = P (Ā) = P (B A) = P (B Ā) = By the Law of Total Probability: P (B) = P (B A)P (A) + P (B Ā)P (Ā). P (B) = =

15 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 15 Now put it all together and use Eq. (2) to obtain: P (A B) = Case b: P (A) = 0.3. Repeat the previous derivations to show that for this case P (A B) = Homework 1.6 In a multiple choice exam, there are 4 answers to a question. A student knows the right answer with probability 0.8 (Case 1), with probability 0.2 (Case 2), and with probability 0.5 (Case 3). If the student does not know the answer s/he always guesses with probability of success being Given that the student marked the right answer, what is the probability he/she knows the answer. Guide: A = Student knows the answer. B = Student marks correctly. Ā = Student does not know the answer. B = Student marks incorrectly. We need to find P (A B). Case 1: We know: P (A) = 0.8. P (Ā) = 0.2. P (B A) = 1. P (B Ā) = By the Law of Total Probability: P (B) = = P (B) = P (B A)P (A) + P (B Ā)P (Ā). Now put it all together and use Eq. (2) to obtain: Case 2: P (A B) = Repeat the previous derivations to obtain: P (A) = 0.2

16 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 16 P (B) = 0.4 P (A B) = 0.5. Case 3: Repeat the previous derivations to obtain: P (A) = 0.5 P (B) = P (A B) = 0.8. Homework 1.7 Watch the following youtube link on the problem known as the Monty Hall problem: Then study the Monty Hall problem in: and understand the different solution approaches and write a computer simulation to estimate the probability of winning the car by always switching. 1.8 Selected Discrete Random Variables We present here several discrete random variables and their corresponding distribution functions. Although our cover here is non-exhaustive, we do consider all the discrete random variables mentioned later in this book Non-parametric Discrete non-parametric random variable X is characterized by a set of n values that X can take with nonzero probability: a 1, a 2,..., a n, and a set of n probability values p 1, p 2,... p n, where p i = P (X = a i ), i = 1, 2,... n. The distribution of X in this case is called a non-parametric distribution because it does not depend on a mathematical function that its shape and range are determined by certain parameters of the distribution Bernoulli We begin with the Bernoulli random variable. It represents an outcome of an experiment which has only two possible outcomes. Let us call them success and failure. These two outcomes are mutually exclusive and exhaustive events. The Bernoulli random variable assigns the value X = 1 to the success outcome and the value X = 0 to the failure outcome. Let p be the probability of the success outcome, and because success and failure are mutually exclusive and exhaustive, the probability of the failure outcome is 1 p. The probability

17 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 17 function in terms of the Bernoulli random variable is: P (X = 1) = p (26) P (X = 0) = 1 p Geometric The geometric random variable X represents the number of independent Bernoulli trials, each of which with p being the probability of success, required until the first success. For X to be equal to i we must have i 1 consecutive failures and then one success in i independent Bernoulli trials. Therefore, we obtain P (X = i) = (1 p) i 1 p for i = 1, 2, 3,.... (27) The complementary distribution function of the geometric random variable is P (X > i) = (1 p) i for i = 0, 1, 2, 3,.... The geometric random variable possesses an important property called memorylessness. particular, discrete random variable X is memoryless if In P (X > m + n X > m) = P (X > n), m = 0, 1, 2,..., and n = 0, 1, 2,... (28) The Geometric random variable is memoryless because it is based on independent Bernoulli trials, and therefore the fact that so far we had m failures does not affect the probability that the next n trials will be failures. The Geometric random variable is the only discrete random variable that is memoryless Binomial Assume that n independent Bernoulli trials are performed. Let X be a random variable representing the number of successes in these n trials. Such random variable is called a binomial random variable with parameters n and p. Its probability function is: ( ) n P (X = i) = p i (1 p) n i i = 0, 1, 2,..., n. i Notice that a Binomial random variable with parameters 1 and p is a Bernoulli random variable. The Bernoulli and binomial random variables have many applications. In particular, it is used as a model for voice as well as data sources. Such sources alternates between two states on and off. During the on state the source is active and transmits at the rate equal to the transmission rate of its equipment (e.g. a modem), and during the off state, the source is idle. If p is the proportion of time that the source is active, and if we consider a superposition of n independent identical sources, than the binomial distribution gives us the probability of the number of sources which are simultaneously active which is important for resource provisioning.

18 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 18 Homework 1.8 Consider a state with voter population N. There are two candidates in the state election for governor and the winner is chosen based on a simple majority. Let N 1 and N 2 be the total number of votes obtained by Candidates 1 and 2, respectively, from voters other than Johnny. Johnny just voted for Candidate 1, and he would like to know the probability that his vote affects the election results. Johnny realizes that the only way he can affect the result of the election is if the votes are equal (without his vote) or the one he voted for (Candidate 1) had (without his vote) one call less than Candidate 2. That is, he tries to find the probability of the event 0 N 1 N 2 1. Assume that any other voter (excluding Johnny) votes independently for Candidates 1 and 2 with probabilities p 1 and p 2, respectively, and also that p 1 + p 2 < 1 to allow for the case that a voter chooses not to vote for either candidate. Derive a formula for the probability that Johnny s vote affects the election results and provide an algorithm and a computer program to compute it for the case N = 2, 000, 000 and p 1 = p 2 = 0.4. Guide By the definition of conditional probability, P (N 1 = n 1, N 2 = n 2 ) = P (N 1 = n 1 )P (N 2 = n 2 N 1 = n 1 ) so ( ) ( ) N 1 P (N 1 = n 1, N 2 = n 2 ) = p n 1 1 (1 p 1 ) N n 1 1 N n1 1 p n 2 2 (1 p 2 ) N n 1 1 n 2. n 1 n 2 Then, as the probability of the union of mutually exclusive events is the sum of their probabilities, the required probability is (N 1)/2 k=0 P (N 1 = k, N 2 = k) + (N 1)/2 1 k=0 P (N 1 = k, N 2 = k + 1). where x is the largest integer smaller or equal to x. and x is the smallest integer greater or equal to x. Next, let us derive the probability distribution of the random variable Y = X 1 + X 2 where X 1 and X 2 are two independent Binomial random variables with parameters (N 1, p) and (N 2, p), respectively. This will require the derivation of the convolution of X 1 and X 2 as follows. P Y (k) = P (X 1 + X 2 = k) k = P ({X 1 = i} {X 2 = k i}) = i=0 k P X1 (i)p X2 (k i) i=0

19 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 19 k ( ) ( ) N1 = p i (1 p) N N2 1 i p k i (1 p) N 2 (k i) i k i i=0 k ( )( ) = p k (1 p) N 1+N 2 k N1 N2 i k i i=0 ( ) = p k (1 p) N 1+N 2 k N1 + N 2. k We can conclude that the convolution of two independent binomial random variables with parameters (N 1, p) and (N 2, p) has a binomial distribution with parameters N 1 + N 2, p. This is not surprising. As we recall the binomial random variable represents the number of successes of a given number of independent Bernoulli trials. Thus, the event of having k Bernoulli successes out of N 1 + N 2 trials is equivalent to the event of having some (or none) of the successes out of the N 1 trials and the remaining out of the N 2 trials. Homework 1.9 In the last step of the above proof, we have used the equality Prove this equality. ( ) N1 + N 2 = k k i=0 ( N1 )( ) N2. i k i Guide Consider the equality (1 + α) N 1+N 2 = (1 + α) N 1 (1 + α) N 2. Then equate the binomial coefficients of α k on both sides Poisson A Poisson random variable with parameter λ has the following probability function: λ λi P (X = i) = e i! i = 0, 1, 2, 3,.... (29) To compute the values of P (X = i), it may be convenient to use the recursion with P (X = i + 1) = P (X = 0) = e λ. λ P (X = i) (30) i + 1

20 Queueing Theory and Stochastic Teletraffic Models c Moshe Zukerman 20 However, if the parameter λ is large, there may be a need to set ˆP (X = λ ) = 1, where x is the largest integer smaller or equal to x, and to compute recursively, using (30) (applied to the ˆP (X = i) s), a sufficient number of ˆP (X = i) values for i > λ and i < λ such that ˆP (X = i) > ɛ, where ɛ is chosen to meet a given accuracy requirement. Clearly the ˆP (X = i) s do not sum up to one, and therefore they do not represent a probability distribution. To approximate the Poisson probabilities they will need to be normalized as follows. Let α and β be the lower and upper bounds, respectively, of the i values for which ˆP (X = i) > ɛ. Then, the probabilities P (X = i) are approximated using the normalization: P (X = i) = { ˆP (X=i) β i=α ˆP (X=i) α i β 0 otherwise. The importance of the Poisson random variable lies in its property to approximate the binomial random variable in case when n is very large and p is very small so that np is not too large and not too small. In particular, consider a sequence of binomial random variables X n, n = 1, 2,... with parameters (n, p) where λ = np, or p = λ/n. Then the probability function (31) lim P (X n = k) n is a Poisson probability function with parameter λ. To prove this we write: Substituting p = λ/n, we obtain ( ) n lim P (X n = k) = lim p k (1 p) n k. n n k or lim P (X n! n = k) = lim n n (n k)!k! ( ) k ( λ 1 λ n k. n n) Now notice that and lim P (X n! n = k) = lim n n (n k)!n k ( λ k k! ( lim 1 λ n = e n n) λ, ( lim 1 λ k = 1, n n) ) ( 1 λ ) n ( 1 λ k. n n)

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