Departure Process from a M/M/m/ Queue

Similar documents
Analysis of Discrete Time Queues (Section 4.6)

Equilibrium Analysis of the M/G/1 Queue

A Performance Model of Space-Division ATM Switches with Input and Output Queueing *

Applied Stochastic Processes

The Dirac Equation. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year

Markov model for analysis and modeling of Distributed Coordination Function of Multirate IEEE Mateusz Wielgosz

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Birth Death Processes

Discrete Memoryless Channels

6. Stochastic processes (2)

6. Stochastic processes (2)

Source-Channel-Sink Some questions

Network of Markovian Queues. Lecture

System in Weibull Distribution

ON A CLASS OF RENEWAL QUEUEING AND RISK PROCESSES

Suggested solutions for the exam in SF2863 Systems Engineering. June 12,

Markov chains. Definition of a CTMC: [2, page 381] is a continuous time, discrete value random process such that for an infinitesimal

Fuzzy approach to solve multi-objective capacitated transportation problem

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model

COS 511: Theoretical Machine Learning

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.

TCOM 501: Networking Theory & Fundamentals. Lecture 7 February 25, 2003 Prof. Yannis A. Korilis

Managing Capacity Through Reward Programs. on-line companion page. Byung-Do Kim Seoul National University College of Business Administration

Advanced Topics in Optimization. Piecewise Linear Approximation of a Nonlinear Function

Chapter 5: Root Locus

EE513 Audio Signals and Systems. Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup

Handling Overload (G. Buttazzo, Hard Real-Time Systems, Ch. 9) Causes for Overload

( ) 2 ( ) ( ) Problem Set 4 Suggested Solutions. Problem 1

Algorithms for factoring

Continuous Time Markov Chains

Hidden Markov Model Cheat Sheet

Queueing Networks II Network Performance

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

Convergence of random processes

On the relationships among queue lengths at arrival, departure, and random epochs in the discrete-time queue with D-BMAP arrivals

Xiangwen Li. March 8th and March 13th, 2001

Excess Error, Approximation Error, and Estimation Error

Applied Mathematics Letters

Notes prepared by Prof Mrs) M.J. Gholba Class M.Sc Part(I) Information Technology

International Journal of Mathematical Archive-9(3), 2018, Available online through ISSN

An application of generalized Tsalli s-havrda-charvat entropy in coding theory through a generalization of Kraft inequality

Chapter 8 Balances on Nonreactive Processes 8.1 Elements of Energy Balances Calculations 8.1a Reference States A Review

Solutions for Homework #9

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Power-sum problem, Bernoulli Numbers and Bernoulli Polynomials.

Journal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article

Expected Value and Variance

Continuous Time Markov Chain

Approccio Statistico all'analisi di Sistemi Caotici e Applicazioni all'ingegneria dell'informazione

Comparing two Quantiles: the Burr Type X and Weibull Cases

Confidence intervals for weighted polynomial calibrations

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Review: Discrete Event Random Processes. Hongwei Zhang

Pattern Classification

On the Calderón-Zygmund lemma for Sobolev functions

On the Transient and Steady-State Analysis of a Special Single Server Queuing System with HOL Priority Scheduling

Order Full Rate, Leadtime Variability, and Advance Demand Information in an Assemble- To-Order System

Interactive Markov Models of Evolutionary Algorithms

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

Response time in a tandem queue with blocking, Markovian arrivals and phase-type services

Mixture of Gaussians Expectation Maximization (EM) Part 2

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES

Introduction to Continuous-Time Markov Chains and Queueing Theory

Identification of Modal Parameters from Ambient Vibration Data by Modified Eigensystem Realization Algorithm *

General Results of Local Metric Dimensions of. Edge-Corona of Graphs

Application of Queuing Theory to Waiting Time of Out-Patients in Hospitals.

A Hybrid Variational Iteration Method for Blasius Equation

Journal of Global Research in Computer Science A MARKOV CHAIN MODEL FOR ROUND ROBIN SCHEDULING IN OPERATING SYSTEM

3. Tensor (continued) Definitions

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models

Priority Queuing with Finite Buffer Size and Randomized Push-out Mechanism

Digital Signal Processing

Determination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm

Assignment 5. Simulation for Logistics. Monti, N.E. Yunita, T.

Module 9. Lecture 6. Duality in Assignment Problems

Structure and Drive Paul A. Jensen Copyright July 20, 2003

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

Quasi-Static transient Thermal Stresses in a Robin's thin Rectangular plate with internal moving heat source

CS-433: Simulation and Modeling Modeling and Probability Review

Fermi-Dirac statistics

Non-Ideality Through Fugacity and Activity

LINEAR REGRESSION ANALYSIS. MODULE VIII Lecture Indicator Variables

EP523 Introduction to QFT I

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Estimation: Part 2. Chapter GREG estimation

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c

On Pfaff s solution of the Pfaff problem

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

Distributions /06. G.Serazzi 05/06 Dimensionamento degli Impianti Informatici distrib - 1

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

Lecture 4: November 17, Part 1 Single Buffer Management

SOJOURN TIME IN A QUEUE WITH CLUSTERED PERIODIC ARRIVALS

CHAPTER 14 GENERAL PERTURBATION THEORY

The Degree Distribution of Random Birth-and-Death Network with Network Size Decline

1 Convex Optimization

Transcription:

Dearture rocess fro a M/M// Queue Q - (-) Q Q3 Q4 (-) Knowledge of the nature of the dearture rocess fro a queue would be useful as we can then use t to analyze sle cases of queueng networs as shown. The ey result here s that the dearture rocess fro a M/M// queue s also osson wth the sae rate as the arrval rate enterng the queue. It should also be noted that the result of randoly slttng or cobnng ndeendent osson rocesses also yelds a osson rocess Coyrght Sanay K. Bose The result on the dearture rocess of a M/M// queue follows fro Bure s Theore. Ths theore states that - [A] The dearture rocess fro a M/M// queue s osson n nature. [B] For a M/M// queue at each te t the nuber of custoers n the syste s ndeendent of the sequence of dearture tes ror to t. [C] For a M/M// FCFS queue gven a custoer dearture at te t the arrval te of ths custoer s ndeendent of the dearture rocess ror to t. Coyrght Sanay K. Bose

Coyrght Sanay K. Bose 3 Te Reversblty roerty of Irreducble Aerodc Marov Chans Consder a dscrete te rreducble aerodc Marov Chan... n- n n... for whch the transton robabltes are gven to be. Now consder the sae chan bacwards n te.e. the chan... n n... 3. Ths would also be a Marov Chan snce we can show that *............... State Transton robablty of the Reverse Chan Coyrght Sanay K. Bose 4 The Marov Chan s consdered to be te reversble for the secal case where *. The reverse chan wll have the followng roertes - The reversed chan s also rreducble and aerodc le the forward chan The reversed chan has the sae statonary state dstrbuton as the forward chan The chan s te reversble only f the detaled balance equaton holds for

How can we handle queues where the servce te dstrbuton s not exonental? [A] If we can exress the actual servce te as cobnatons of exonentally dstrbuted te ntervals then the Method of Stages ay be used. (Secton.9) [B] The M/G/ queue and ts varatons ay be analyzed. (Chaters 3 and 4) [C] Aroxaton ethods ay be used f the ean and varance of the servce te are gven. (GI/G/ aroxaton of Secton 6.) Coyrght Sanay K. Bose 5 Method of Stages Stage /µ Stage /µ Consder a M/-// exale where the actual servce te s the su of two rando varables each of whch s exonentally dstrbuted. State of the syste reresented as (n ) where n s the total nuber of custoers n the syste where the custoer currently beng served s at Stage n... State () reresents the state when the syste s ety () ( ) ( ) µ µ State Transton µ µ Dagra of the Syste ( ) ( ) Coyrght Sanay K. Bose 6 3

Balance Equatons for the Syste µ ( µ ) ( µ ( µ ) ( µ etc... ) ) µ µ µ µ 3 (.38) These Balance Equatons ay be solved along wth the arorate Noralzaton Condton to obtan the state robabltes of the syste. Once these are nown erforance araeters of the queue ay be arorately evaluated. Coyrght Sanay K. Bose 7 The ethod llustrated for the M/-// exale ay be extended for the followng tyes systes.. Have stages of servce tes - ore rows n the state transton dagra. Fnte Nuber of Watng ostons n the Queue - ae the arrval rate a functon of the nuber n the syste and ae t go to zero once all the watng ostons have been flled 3. Multle Servers - aroxate ths by allowng ore than one ob to enter servce at a te 4. More General Servce Te Dstrbutons - see next slde Coyrght Sanay K. Bose 8 4

For ore general servce te dstrbutons the Method of Stages ay be used f the Lalace Transfor of the df of the servce te ay be reresented as a ratonal functon of s L B (s)n(s)/d(s) wth sle roots. Entry Stage µ α α -α -α Stage µ Ths leads to - L ( s) ( α B Ext Wth ultle stages le ths the L.T. of the servce te df wll be of the for - ) α... α ( α ) µ s µ L ( s) B β β s µ Coyrght Sanay K. Bose 9 Gven a servce te df as L B (s)n(s)/d(s) wth sle roots -. Obtan the ultle stage reresentaton n the for shown earler. Draw the corresondng state transton dagra and dentfy the flows between the varous states 3. Wrte and solve the flow balance equatons along wth the noralzaton condton to obtan the state robabltes 4. Use the state robabltes to obtan the requred erfroance araeters Coyrght Sanay K. Bose 5

Queues wth Bul (or Batch) Arrvals (Secton.) M [] osson Batch Arrval rocess Batches arrvng as a osson rocess wth exonentally dstrbuted nter-arrval tes between batches Batch sze Nuber of obs n a batch (rando varable) Average Batch Arrval Rate β r r obs n a batch r. β ( β r z r β r rβ r r Coyrght Sanay K. Bose The M [] /M/ Queue µ ( for ) µ β for Balance Equatons Though these ay be solved n the standard fashon we wll consder a soluton aroach for drectly obtanng ( the Generatng Functon for the nuber n the syste. For ths we would need to ultly the th equaton above by z and su fro to. ( µ ) z µ z z β z ( n n n z Coyrght Sanay K. Bose 6

Slfyng we get µ ( µ )[ ( ] [ ( z µ ( ( µ ( z[ β( ] z] ( β ( β Defne ρ as the offered traffc µ Note that () s effectvely the sae as the Noralzaton Condton. Usng ths we get ρ µ ( ρ)( ( µ ( z[ β ( ] Therefore (.4) We can nvert ( or exand t as a ower seres n z to get the state robablty dstrbuton. The ean nuber N n the syste ay be drectly calculated fro ( as - d( ρ( β β ) N (.43) dz ( ) z ρ Coyrght Sanay K. Bose 3 The M [] /-/-/K Queue Batch Arrval Queue wth Fnte Caacty For oeratng queues of ths tye one ust also secfy the batch accetance strategy to be followed f a batch of sze or ore arrrves n a syste where the nuber of watng ostons avalable s less than. artal Batch Accetance Strategy (BAS) Whole Batch Accetance Strategy (WBAS) Randoly choose as any obs fro the batch as ay be accoodated n the buffer Accet the batch only f all ts obs ay be accoodated; otherwse reect all obs of the batch Coyrght Sanay K. Bose 4 7

M [ /M/-/- tyes of queues ay be oerated and analyzed under ether the BAS or the WBAS strategy See Secton. where ths analyss s done for a M [ /M/s/s queue. The state dstrbuton for ths queue are gven by φ... s µ (.46) where φ β... Coyrght Sanay K. Bose 5 8