THE INTERCHANGEABILITY OF./M/1 QUEUES IN SERIES. 1. Introduction

Similar documents
Performance Evaluation of Queuing Systems

On the Pathwise Optimal Bernoulli Routing Policy for Homogeneous Parallel Servers

2905 Queueing Theory and Simulation PART III: HIGHER DIMENSIONAL AND NON-MARKOVIAN QUEUES

School of Electrical Engineering, Phillips Hall, Cornell University, Ithaca, NY 14853, U.S.A.

IEOR 6711: Stochastic Models I, Fall 2003, Professor Whitt. Solutions to Final Exam: Thursday, December 18.

Queueing Theory I Summary! Little s Law! Queueing System Notation! Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K "

Inequality Comparisons and Traffic Smoothing in Multi-Stage ATM Multiplexers

6 Solving Queueing Models

15 Closed production networks

Lecture 20: Reversible Processes and Queues

15 Closed production networks

Data analysis and stochastic modeling

Queuing Analysis of Markovian Queue Having Two Heterogeneous Servers with Catastrophes using Matrix Geometric Technique

Part I Stochastic variables and Markov chains

Contents Preface The Exponential Distribution and the Poisson Process Introduction to Renewal Theory

On the static assignment to parallel servers

Markov Processes and Queues

ON THE LAW OF THE i TH WAITING TIME INABUSYPERIODOFG/M/c QUEUES

A MONOTONICITY RESULT FOR A G/GI/c QUEUE WITH BALKING OR RENEGING

A Simple Solution for the M/D/c Waiting Time Distribution

Time Reversibility and Burke s Theorem

Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals

IEOR 6711, HMWK 5, Professor Sigman

LIMITS FOR QUEUES AS THE WAITING ROOM GROWS. Bell Communications Research AT&T Bell Laboratories Red Bank, NJ Murray Hill, NJ 07974

THE ON NETWORK FLOW EQUATIONS AND SPLITTG FORMULAS TRODUCTION FOR SOJOURN TIMES IN QUEUEING NETWORKS 1 NO FLOW EQUATIONS

Synchronized Queues with Deterministic Arrivals

Queuing Networks: Burke s Theorem, Kleinrock s Approximation, and Jackson s Theorem. Wade Trappe

Markov Chains. X(t) is a Markov Process if, for arbitrary times t 1 < t 2 <... < t k < t k+1. If X(t) is discrete-valued. If X(t) is continuous-valued

Introduction to Queuing Networks Solutions to Problem Sheet 3

Chapter 2. Poisson Processes. Prof. Shun-Ren Yang Department of Computer Science, National Tsing Hua University, Taiwan

The exponential distribution and the Poisson process

Queueing. Chapter Continuous Time Markov Chains 2 CHAPTER 5. QUEUEING

QUEUING SYSTEM. Yetunde Folajimi, PhD

MATH 56A: STOCHASTIC PROCESSES CHAPTER 6

EFFICIENT COMPUTATION OF PROBABILITIES OF EVENTS DESCRIBED BY ORDER STATISTICS AND APPLICATION TO A PROBLEM OFQUEUES

Stability of the two queue system

Continuous Time Processes

One important issue in the study of queueing systems is to characterize departure processes. Study on departure processes was rst initiated by Burke (

Recap. Probability, stochastic processes, Markov chains. ELEC-C7210 Modeling and analysis of communication networks

Figure 10.1: Recording when the event E occurs

Since D has an exponential distribution, E[D] = 0.09 years. Since {A(t) : t 0} is a Poisson process with rate λ = 10, 000, A(0.

CHAPTER 3 STOCHASTIC MODEL OF A GENERAL FEED BACK QUEUE NETWORK

CPSC 531: System Modeling and Simulation. Carey Williamson Department of Computer Science University of Calgary Fall 2017

7 Variance Reduction Techniques

Introduction to Queueing Theory with Applications to Air Transportation Systems

Stochastic process. X, a series of random variables indexed by t

Little s result. T = average sojourn time (time spent) in the system N = average number of customers in the system. Little s result says that

Queueing Theory II. Summary. ! M/M/1 Output process. ! Networks of Queue! Method of Stages. ! General Distributions

THE HEAVY-TRAFFIC BOTTLENECK PHENOMENON IN OPEN QUEUEING NETWORKS. S. Suresh and W. Whitt AT&T Bell Laboratories Murray Hill, New Jersey 07974

Analysis of an M/G/1 queue with customer impatience and an adaptive arrival process

A tandem queueing model with coupled processors

Solutions to Homework Discrete Stochastic Processes MIT, Spring 2011

TCOM 501: Networking Theory & Fundamentals. Lecture 6 February 19, 2003 Prof. Yannis A. Korilis

Q = (c) Assuming that Ricoh has been working continuously for 7 days, what is the probability that it will remain working at least 8 more days?

Stationary remaining service time conditional on queue length

Notes on uniform convergence

OPTIMAL CONTROL OF A FLEXIBLE SERVER

CDA5530: Performance Models of Computers and Networks. Chapter 4: Elementary Queuing Theory

λ λ λ In-class problems

A STAFFING ALGORITHM FOR CALL CENTERS WITH SKILL-BASED ROUTING: SUPPLEMENTARY MATERIAL

Other properties of M M 1

Introduction to Queueing Theory

RELATING TIME AND CUSTOMER AVERAGES FOR QUEUES USING FORWARD COUPLING FROM THE PAST

Introduction to Queueing Theory

A Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks

The Transition Probability Function P ij (t)

Multiserver Queueing Model subject to Single Exponential Vacation

1 IEOR 4701: Continuous-Time Markov Chains

CS 798: Homework Assignment 3 (Queueing Theory)

Batch Arrival Queuing Models with Periodic Review

On Tandem Blocking Queues with a Common Retrial Queue

Chapter 3: Markov Processes First hitting times

Queueing Theory. VK Room: M Last updated: October 17, 2013.

Section 3.3: Discrete-Event Simulation Examples

Introduction to Queueing Theory

Analysis of a Two-Phase Queueing System with Impatient Customers and Multiple Vacations

Computer Networks More general queuing systems

HEAVY-TRAFFIC EXTREME-VALUE LIMITS FOR QUEUES

Queueing Review. Christos Alexopoulos and Dave Goldsman 10/6/16. (mostly from BCNN) Georgia Institute of Technology, Atlanta, GA, USA

Queueing Review. Christos Alexopoulos and Dave Goldsman 10/25/17. (mostly from BCNN) Georgia Institute of Technology, Atlanta, GA, USA

LECTURE #6 BIRTH-DEATH PROCESS

1 Basic concepts from probability theory

Universal examples. Chapter The Bernoulli process

BIRTH DEATH PROCESSES AND QUEUEING SYSTEMS

5/15/18. Operations Research: An Introduction Hamdy A. Taha. Copyright 2011, 2007 by Pearson Education, Inc. All rights reserved.

Equivalent Models and Analysis for Multi-Stage Tree Networks of Deterministic Service Time Queues

Advanced Queueing Theory

The shortest queue problem

Improved Algorithms for Machine Allocation in Manufacturing Systems

Queuing Theory. Using the Math. Management Science

Chapter 1. Introduction. 1.1 Stochastic process

Queuing Theory. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011

An M/M/1 Queue in Random Environment with Disasters

Exponential Distribution and Poisson Process

Elementary queueing system

On Tandem Blocking Queues with a Common Retrial Queue

Chapter 5. Continuous-Time Markov Chains. Prof. Shun-Ren Yang Department of Computer Science, National Tsing Hua University, Taiwan

ec1 e-companion to Liu and Whitt: Stabilizing Performance

On optimization of a Coxian queueing model with two phases

M/G/1 and Priority Queueing

Transcription:

THE INTERCHANGEABILITY OF./M/1 QUEUES IN SERIES J. Appl. Prob. 16, 690-695 (1979) Printed in Israel? Applied Probability Trust 1979 RICHARD R. WEBER,* University of Cambridge Abstract A series of queues consists of a number of./m/1 queues arranged in a series order. Each queue has an infinite waiting room and a single exponential server. The rates of the servers may differ. Initially the system is empty. Customers enter the first queue according to an arbitrary stochastic input process and then pass through the queues in order: a customer leaving the first queue immediately enters the second queue, and so on. We are concerned with the stochastic output process of customer departures from the final queue. We show that the queues are interchangeable, in the sense that the output process has the same distribution for all series arrangements of the queues. The 'output theorem' for the M/M/1 queue is a corollary of this result. OUTPUT PROCESSES; DEPARTURE PROCESSES; SERIES OF QUEUES; TANDEM QUEUES 1. Introduction Series of queues are interesting to study because they form one of the simplest of queueing systems and are a fundamental element in many more complicated queueing networks. A system of queues in series (or queues in tandem), denoted by -/M1/1 - /M2/1 --* /MN/1, consists of N queues of./m/1 type arranged in a series order. Each queue has an infinite waiting room and a single exponential (memoryless) server. The rates of the servers may differ. Initially the system is empty. Customers enter the first queue according to an arbitrary stochastic input process and then pass through the queues in order: a customer leaving the first queue immediately enters the second queue, and so on. The number of customers to enter the first queue in the interval [0, t) is given by the random function A (t). We are concerned with the stochastic output process of customer departures from the final queue, and the proof that its distribution is the same for all series arrangements of the queues. We say that the queues are interchangeable, meaning that the order of the queues does not affect the distribution of any statistic of the output process (such as the time of the nth customer departure). Results concerning the outputs of series of queues have been surveyed by Received 6 June 1978; revision received 4 July 1978. *Postal address: Queens' College, Cambridge CB3 9ET, U.K. 690

The interchangeability of tandem / M /1 queues in series 691 Reich (1965) and Boxma (1977). If the servers are deterministic rather than exponential a result of Friedman (1965) shows that the queues are interchangeable. This is a purely combinatoric result. For exponential servers, the 'output theorem' of Burke (1956) states that the stationary distribution of the output of an unsaturated M/M/1 queue is a Poisson process with the same rate as the input. This means that if the input process is Poisson, with a rate less than that of any server, then the stationary output of the - M/M,/1 /M2/1 -- -/MN/1 system is the same for all orderings of the queues, being a Poisson process with the same rate as the input. But this tells us nothing about how the ordering of the queues might affect the transient output process (for example, the departure time of the nth customer); neither does it tell us what happens if the input process is not Poisson. Our interchangeability theorem holds for any input process and shows that the full output process is independent of the order of the queues. 2. The interchangeability theorem It is the result of this section that, with an arbitrary input process, a number of?/m/1 queues in series are interchangeable. We directly prove interchangeability for just two queues in series; but this suffices to prove it for any number. For suppose two queues in series are interchangeable. Notice that the input to a series of two queues might itself be the output of an 'upstream' series of queues. So two orderings of N queues in series have identically distributed outputs if they differ in the interchange of one adjacent pair of queues. By the interchange of adjacent pairs of queues we see that N queues in series are interchangeable. It now suffices to show that two queues in series are interchangeable. Theorem. Two./M/1 queues in series are interchangeable: for any common input process, A (t), the output processes of /Mi/1/ -- M2/ 1 and./m2/1 ->/M/1 have the same distribution. Proof. It is sufficient to prove the theorem for arbitrary non-stochastic input processes. For if the theorem is true for every non-stochastic realization of the stochastic input process A (t), then it is true for that stochastic input process. So suppose that no customers are present before time 0 and that the non-stochastic input process is such that the number of customers entering the first queue in the interval [0, t) is a (t). We say that two queues in series are in state (t, m, n), when the time is t, the first queue contains a(t)- m - n customers, the second queue contains m customers, and n customers have already departed (customers which arrive at time t are considered to be 'just arriving'). Throughout what follows the phrase, 'for all (t, m, n)', is taken as meaning, 'for all states (t, m, n) which can be reached from the state (,0, 0,0)'. We consider the departure times of the first n*

692 RICHARD R. WEBER customers and show that their joint distribution is the same for both orderings of the queues. Consider first the system./m1/l - /M2/1, and suppose that the first server has rate A and the second server has rate /, where without loss of generality A +, = 1. Suppose the system is in state (t, m, n) with n < n*. Starting from this state, let D (t, m, n) be the departure time of the jth customer (j = n + 1, *, n*). Define the joint Laplace transform of the departure times of these n *- n customers as FA(t, m, n) = E[exp{- O+,1DA+,(t, m, n)- -OnD...(t, m, n)}], On,+1,-, On, real and positive. Although FP(t, m, n) is a function of the variables On+ i,,*', we omit their explicit mention for notational convenience. For n _ n* define F(t, m, n) = 1. Similarly define F (t, m, n) for the system -/M2/1--/M1/1. To prove the theorem it is sufficient to show that FA (, 0, 0) = F (0, 0, 0), since if these two Laplace transforms are equal the joint distribution of the departure times of the first n* customers are the same for both orderings of the queues. The equality of the Laplace transforms is proved by constructing, for all (t, m, n), FP(t, m, n) and F (t, m, n), which tend to F(t, m, n) and F (t, m, n) respectively as k tends to oo. We then show by induction on k that F(t, m, n)= F (t, m,n) for all k. Consider the system /M1/1->/M2/1. When both servers are busy the time until a customer completes service in one or the other of the queues is distributed as an exponential random variable with mean 1 (A +,u = 1). This suggests the following device. We suppose that in addition to the 'real' customers already considered the system contains an infinite number of 'virtual' customers in each queue. A server serves virtual customers whenever there are no real customers in its queue, but as soon as a real customer enters the queue the server attends to that real customer. So the presence of virtual customers makes no difference to the real ones. But by this device the servers are always busy and the time until the completion of a customer (real or virtual) in one or the other of the queues is always distributed as an exponential random variable with mean 1. The completion occurs in the first queue with probability A and in the second queue with probability /. By considering this first service completion, we have for all (t,m,n) with n< n*, (1) F(t, m, n) = {AFA(s, m + 1, n) + /FA(s, m - 1, n + 1)e -.+}e-(s-ds, with the provisions that (i) if m + n = a(s), then FA(s, m + 1, n) is replaced by F(s, m, n), and (ii) if m = O, then FA(s, m - 1, n + 1)e-?n+s" is replaced by F(s, O, n).

The interchangeability of tandem / M / 1 queues in series 693 The provisions (i) and (ii) apply to cases where the first service completion is of a virtual customer in the first or second queue. A similar formula holds for F (t, m, n). For all (t, m, n) with n n* define F(t, m, n) = 1 (k = 0, 1,2, - ). For all (t, m, n) with n < n define tn -= - 1 + '-+ On,* F((t, m,n)= e-"', and for k =0, 1,2, -, F,(t, m, n) (2) {AF (s, m + 1, n) +- /F (t, m - 1, n + 1)e- }e -"-ds, with similar provisions on the terms within the integral as in (1). Similarly define F (t, m, n). Clearly 0 - F (t, m, n)- FA (t,m, n) - exp ( - t). By subtracting (1) from (2) it is easy to show inductively that F.(t, m, n ki '? F (t, m, n). -k-- 0 as k--c. k,~ (1? - O.) The proof of the theorem will be complete if we can show that F (0,0, 0)= F (0,0,0). In fact, we shall show that for all (t,m,n) and k, (3) S'(t,m,n)= SI(t,m,n), where S (t,m,n) is defined by Sk(t,m,n)= F(t, n)= m, A FA'F(t, m-i i=o i, n). Note that S (t,0, n) = Fk (t,, n). We similarly define S (t, m,n), and then prove (3) by induction on k. For k = 0, (3) is true since SO(t,m,n)= S ((t, m, n) = exp ( - nt). Suppose that (3) is true for k; we shall show that it is true for k + 1. Summing (2) we have S l?(t,m, n) = f {A+1FF(s, m + 1, n) + aa m'f(s, k m - 1 n + l)e -S J+ m-i + /,i+a','fa(s,i + 1,n) -= 0, (s i + Fk (s, 0, n) + A'F(s, i - 1, n + 1)ee +s e-"-'ds. By writing, P2Fk (s, 0, n) as ak (s, 0, n) -,AS k (s, 0, n) and appropriately combining the terms within the integral we have

694 RICHARD R. WEBER (4) Sk+, (t, m, n)= {S(s, m + 1, n)- Sk (s,o, n) + pas,(s, m - 1, n + l)e -+S}e- "ds, with the provisions that: (i) if m + n = a (s), then S (s, m + 1, n) is replaced by S (s, m, n), and (ii) if m = 0, then the last two terms in the integral are deleted. By the inductive hypothesis for (3) every term within the integral of (4) is unchanged in value when the superscript A is replaced by L. Hence Sk+l(tm, mn) = Sk+i(t, m, n) for all (t, m, n), and the induction is complete. In particular. Sk (0,0,0) = S` (0,0,0) for all k. This is just Fk (0,0,0)= Fk (0,0,0). Letting k -> oo we deduce that FA (0, 0, 0) = F (0, 0, 0). This completes the proof of the theorem. 3. Applications Burke's 'output theorem' for the M/M/1 queue may be easily deduced from our theorem. We do this by noticing that an M/M/1 queue can be viewed as two?/m/1 queues in series, where the first queue contains an infinite number of customers. Suppose that the queue M1/M2/1 has an arrival rate A less than the service rate,i. Our interchangeability theorem tells us that the output processes of MJM2/1 and MJMi/l have the same distribution. But M2/M1/I saturates and its stationary output is just a Poisson process of rate A. So the stationary output of M1/M21/ is also Poisson of rate A. It is clear that the interchangeability theorem continues to hold even when the input process is a function of the output process. A special case of this is a cycle of queues, formed by taking a series of queues and sending the output back into the first queue as input. In order that the cycle not saturate, we suppose that the process of external input, A(t), terminates at some fixed time. We deduce the interesting result that if a fixed number of customers circle unendingly around a cycle of exponential server queues, then the outputs from all the queues have the same stationary distribution; moreover, this distribution is independent of the order of the queues. We might reverse our model by thinking of the N servers as passing through n customers in series. This enables us to consider the sequencing of N nonidentical 'jobs' through a flowshop of n identical 'machines'. We find that the time for all the jobs to pass through the n machines is independent of the order in which they are served at each machine. The mean finish time of the N jobs is minimized by passing them through the machines in 'shortest first' order. A number of./d/1 (deterministic) queues in series are interchangeable. But the queues in a series of mixed I/D/1 and./m/1 queues are not interchangeable.

The interchangeability of tandem / M / 1 queues in series 695 If a -/D/1 queue is last in such a series, then there is a minimum interval between one departure and the next. This is not the case if a I/M/1 queue is last. Burke's output theorem is true for the M/M/s queue. But a number of./m/s queues in series are not interchangeable, as any simple example will show. For a discussion of the optimal ordering of non-interchangeable queues see Tembe and Wolff (1974). References BOXMA, O. J. (1977) Analysis of Models for Tandem Queues. Ph.D. Thesis, University of Utrecht. BURKE, P. J. (1956) The output of a queueing system. Opns Res. 4, 699-704. FRIEDMAN, H. D. (1965) Reduction methods for tandem queueing systems. Opns Res. 13, 121-131. REICH, E. (1965) Departure processes. In Proceedings of the Symposium on Congestion Theory, University of North Carolina Press, Chapel Hill, 439-457. TEMBE, S. V. AND WOLFF, R. W. (1974) The optimal order of service in tandem queues. Opns Res. 22, 824-832.