Queueing Systems with Periodic Service
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Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems·
1 Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems· Onno Boxmat, Ger Koolet & Zhen Liui t CW/, Amsterdam, The Netherlands t INRIA-Sophia Antipolis, France This paper aims to give an overview of solution methods for the performance analysis of parallel and distributed systems. After a brief review of some important general solution methods, we discuss key models of parallel and distributed systems, and optimization issues, from the viewpoint of solution methodology. 1 INTRODUCTION The purpose of this paper is to present a survey of queueing theoretic methods for the quantitative modeling and analysis of parallel and distributed systems. We discuss a number of queueing models that can be viewed as key models for the performance analysis and optimization of parallel and distributed systems. Most of these models are very simple, but display an essential feature of dis tributed processing. In their simplest form they allow an exact analysis. We explore the possibilities and limitations of existing solution methods for these key models, with the purpose of obtaining insight into the potential of these solution methods for more realistic complex quantitative models. As far as references is concerned, we have restricted ourselves in the text mainly to key references that make a methodological contribution, and to sur veys that give the reader further access to the literature; we apologize for any inadvertent omissions. The reader is referred to Gelenbe's book [65] for a general introduction to the area of multiprocessor performance modeling and analysis. Stochastic Petri nets provide another formalism for modeling and perfor mance analysis of discrete event systems. -
A Note on the Screaming Toes Game
A note on the Screaming Toes game Simon Tavar´e1 1Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, USA June 11, 2020 Abstract We investigate properties of random mappings whose core is composed of derangements as opposed to permutations. Such mappings arise as the natural framework to study the Screaming Toes game described, for example, by Peter Cameron. This mapping differs from the classical case primarily in the behaviour of the small components, and a number of explicit results are provided to illustrate these differences. Keywords: Random mappings, derangements, Poisson approximation, Poisson-Dirichlet distribu- tion, probabilistic combinatorics, simulation, component sizes, cycle sizes MSC: 60C05,60J10,65C05, 65C40 1 Introduction The following problem comes from Peter Cameron’s book, [4, p. 154]. n people stand in a circle. Each player looks down at someone else’s feet (i.e., not at their own feet). At a given signal, everyone looks up from the feet to the eyes of the person they were looking at. If two people make eye contact, they scream. What is the probability qn, say, of at least one pair screaming? The purpose of this note is to put this problem in its natural probabilistic setting, namely that of a random mapping whose core is a derangement, for which many properties can be calculated simply. We focus primarily on the small components, but comment on other limiting regimes in the discussion. We begin by describing the usual model for a random mapping. Let B1,B2,...,Bn be indepen- arXiv:2006.04805v1 [math.PR] 8 Jun 2020 dent and identically distributed random variables satisfying P(B = j)=1/n, j [n], (1) i ∈ where [n] = 1, 2,...,n . -
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This article was downloaded by: [informa internal users] On: 4 September 2009 Access details: Access Details: [subscription number 755239602] Publisher Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Communications in Statistics - Theory and Methods Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713597238 Sequential Testing to Guarantee the Necessary Sample Size in Clinical Trials Andrew L. Rukhin a a Statistical Engineering Division, Information Technologies Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, USA Online Publication Date: 01 January 2009 To cite this Article Rukhin, Andrew L.(2009)'Sequential Testing to Guarantee the Necessary Sample Size in Clinical Trials',Communications in Statistics - Theory and Methods,38:16,3114 — 3122 To link to this Article: DOI: 10.1080/03610920902947584 URL: http://dx.doi.org/10.1080/03610920902947584 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. -
Italic Entries Indicatefigures. 319-320,320
Index A Analytic hierarchy process, 16-24, role of ORIMS, 29-30 17-18 Availability,31 A * algorithm, 1 absolute, relative measurement Averch-lohnson hypothesis, 31 Acceptance sampling, 1 structural information, 17 Accounting prices. 1 absolute measurement, 23t, 23-24, Accreditation, 1 24 B Active constraint, 1 applications in industry, Active set methods, 1 government, 24 Backward chaining, 33 Activity, 1 comments on costlbenefit analysis, Backward Kolmogorov equations, 33 Activity-analysis problem, 1 17-19 Backward-recurrence time, 33 Activity level, 1 decomposition of problem into Balance equations, 33 Acyclic network, 1 hierarchy, 21 Balking, 33 Adjacency requirements formulation, eigenvector solution for weights, Banking, 33-36 facilities layout, 210 consistency, 19-20, 20t banking, 33-36 Adjacent, 1 employee evaluation hierarchy, 24 portfolio diversification, 35-36 Adjacent extreme points, 1 examples, 21-24 portfolio immunization, 34-35 Advertising, 1-2, 1-3 fundamental scale, 17, 18, 18t pricing contingent cash flows, competition, 3 hierarchic synthesis, rank, 20-21 33-34 optimal advertising policy, 2-3 pairwise comparison matrix for BarChart, 37 sales-advertising relationship, 2 levell, 21t, 22 Barrier, distance functions, 37-39 Affiliated values bidding model, 4 random consistency index, 20t modified barrier functions, 37-38 Affine-scaling algorithm, 4 ranking alternatives, 23t modified interior distance Affine transformation, 4 ranking intensities, 23t functions, 38-39 Agency theory, 4 relative measurement, 21, 21-22t, Basic feasible solution, 40 Agriculture 21-24 Basic solution, 40 crop production problems at farm structural difference between Basic vector, 40, 41 level,429 linear, nonlinear network, 18 Basis,40 food industry and, 4-6 structuring hierarchy, 20 Basis inverse, 40 natural resources, 428-429 synthesis, 23t Batch shops, 41 regional planning problems, 429 three level hierarchy, 17 Battle modeling, 41-44 AHP, 7 Animation, 24 attrition laws, 42-43 AI, 7. -
Chapter 8 - Frequently Used Symbols
UNSIGNALIZED INTERSECTION THEORY BY ROD J. TROUTBECK13 WERNER BRILON14 13 Professor, Head of the School, Civil Engineering, Queensland University of Technology, 2 George Street, Brisbane 4000 Australia. 14 Professor, Institute for Transportation, Faculty of Civil Engineering, Ruhr University, D 44780 Bochum, Germany. Chapter 8 - Frequently used Symbols bi = proportion of volume of movement i of the total volume on the shared lane Cw = coefficient of variation of service times D = total delay of minor street vehicles Dq = average delay of vehicles in the queue at higher positions than the first E(h) = mean headway E(tcc ) = the mean of the critical gap, t f(t) = density function for the distribution of gaps in the major stream g(t) = number of minor stream vehicles which can enter into a major stream gap of size, t L = logarithm m = number of movements on the shared lane n = number of vehicles c = increment, which tends to 0, when Var(tc ) approaches 0 f = increment, which tends to 0, when Var(tf ) approaches 0 q = flow in veh/sec qs = capacity of the shared lane in veh/h qm,i = capacity of movement i, if it operates on a separate lane in veh/h qm = the entry capacity qm = maximum traffic volume departing from the stop line in the minor stream in veh/sec qp = major stream volume in veh/sec t = time tc = critical gap time tf = follow-up times tm = the shift in the curve Var(tc ) = variance of critical gaps Var(tf ) = variance of follow-up-times Var (W) = variance of service times W = average service time. -
VGAM Package. R News, 8, 28–39
Package ‘VGAM’ January 14, 2021 Version 1.1-5 Date 2021-01-13 Title Vector Generalized Linear and Additive Models Author Thomas Yee [aut, cre], Cleve Moler [ctb] (author of several LINPACK routines) Maintainer Thomas Yee <[email protected]> Depends R (>= 3.5.0), methods, stats, stats4, splines Suggests VGAMextra, MASS, mgcv Enhances VGAMdata Description An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book ``Vector Generalized Linear and Additive Models: With an Implementation in R'' (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. License GPL-3 URL https://www.stat.auckland.ac.nz/~yee/VGAM/ NeedsCompilation yes 1 2 R topics documented: BuildVignettes yes LazyLoad yes LazyData yes Repository CRAN Date/Publication 2021-01-14 06:20:05 UTC R topics documented: VGAM-package . -
Approximating Heavy Traffic with Brownian Motion
APPROXIMATING HEAVY TRAFFIC WITH BROWNIAN MOTION HARVEY BARNHARD Abstract. This expository paper shows how waiting times of certain queuing systems can be approximated by Brownian motion. In particular, when cus- tomers exit a queue at a slightly faster rate than they enter, the waiting time of the nth customer can be approximated by the supremum of reflected Brow- nian motion with negative drift. Along the way, we introduce fundamental concepts of queuing theory and Brownian motion. This paper assumes some familiarity of stochastic processes. Contents 1. Introduction 2 2. Queuing Theory 2 2.1. Continuous-Time Markov Chains 3 2.2. Birth-Death Processes 5 2.3. Queuing Models 6 2.4. Properties of Queuing Models 7 3. Brownian Motion 8 3.1. Random Walks 9 3.2. Brownian Motion, Definition 10 3.3. Brownian Motion, Construction 11 3.3.1. Scaled Random Walks 11 3.3.2. Skorokhod Embedding 12 3.3.3. Strong Approximation 14 3.4. Brownian Motion, Variants 15 4. Heavy Traffic Approximation 17 Acknowledgements 19 References 20 Date: November 21, 2018. 1 2 HARVEY BARNHARD 1. Introduction This paper discusses a Brownian Motion approximation of waiting times in sto- chastic systems known as queues. The paper begins by covering the fundamental concepts of queuing theory, the mathematical study of waiting lines. After waiting times and heavy traffic are introduced, we define and construct Brownian motion. The construction of Brownian motion in this paper will be less thorough than other texts on the subject. We instead emphasize the components of the construction most relevant to the final result—the waiting time of customers in a simple queue can be approximated with Brownian motion. -
Concentration Inequalities from Likelihood Ratio Method
Concentration Inequalities from Likelihood Ratio Method ∗ Xinjia Chen September 2014 Abstract We explore the applications of our previously established likelihood-ratio method for deriving con- centration inequalities for a wide variety of univariate and multivariate distributions. New concentration inequalities for various distributions are developed without the idea of minimizing moment generating functions. Contents 1 Introduction 3 2 Likelihood Ratio Method 4 2.1 GeneralPrinciple.................................... ...... 4 2.2 Construction of Parameterized Distributions . ............. 5 2.2.1 WeightFunction .................................... .. 5 2.2.2 ParameterRestriction .............................. ..... 6 3 Concentration Inequalities for Univariate Distributions 7 3.1 BetaDistribution.................................... ...... 7 3.2 Beta Negative Binomial Distribution . ........ 7 3.3 Beta-Prime Distribution . ....... 8 arXiv:1409.6276v1 [math.ST] 1 Sep 2014 3.4 BorelDistribution ................................... ...... 8 3.5 ConsulDistribution .................................. ...... 8 3.6 GeetaDistribution ................................... ...... 9 3.7 GumbelDistribution.................................. ...... 9 3.8 InverseGammaDistribution. ........ 9 3.9 Inverse Gaussian Distribution . ......... 10 3.10 Lagrangian Logarithmic Distribution . .......... 10 3.11 Lagrangian Negative Binomial Distribution . .......... 10 3.12 Laplace Distribution . ....... 11 ∗The author is afflicted with the Department of Electrical -
Queueing Systems
Queueing Systems Ivo Adan and Jacques Resing Department of Mathematics and Computing Science Eindhoven University of Technology P.O. Box 513, 5600 MB Eindhoven, The Netherlands March 26, 2015 Contents 1 Introduction7 1.1 Examples....................................7 2 Basic concepts from probability theory 11 2.1 Random variable................................ 11 2.2 Generating function............................... 11 2.3 Laplace-Stieltjes transform........................... 12 2.4 Useful probability distributions........................ 12 2.4.1 Geometric distribution......................... 12 2.4.2 Poisson distribution........................... 13 2.4.3 Exponential distribution........................ 13 2.4.4 Erlang distribution........................... 14 2.4.5 Hyperexponential distribution..................... 15 2.4.6 Phase-type distribution......................... 16 2.5 Fitting distributions.............................. 17 2.6 Poisson process................................. 18 2.7 Exercises..................................... 20 3 Queueing models and some fundamental relations 23 3.1 Queueing models and Kendall's notation................... 23 3.2 Occupation rate................................. 25 3.3 Performance measures............................. 25 3.4 Little's law................................... 26 3.5 PASTA property................................ 27 3.6 Exercises..................................... 28 4 M=M=1 queue 29 4.1 Time-dependent behaviour........................... 29 4.2 Limiting behavior............................... -
A Survey on Performance Analysis of Warehouse Carousel Systems
A Survey on Performance Analysis of Warehouse Carousel Systems N. Litvak∗, M. Vlasiou∗∗ August 6, 2018 ∗ Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Applied Mathematics, University of Twente, 7500 AE Enschede, The Netherlands. ∗∗ Eurandom and Department of Mathematics & Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. [email protected], [email protected] Abstract This paper gives an overview of recent research on the performance evaluation and design of carousel systems. We discuss picking strategies for problems involving one carousel, consider the throughput of the system for problems involving two carousels, give an overview of related problems in this area, and present an extensive literature review. Emphasis has been given on future research directions in this area. Keywords: order picking, carousels systems, travel time, throughput AMS Subject Classification: 90B05, 90B15 1 Introduction A carousel is an automated storage and retrieval system, widely used in modern warehouses. It consists of a number of shelves or drawers, which are linked together and are rotating in a closed loop. It is operated by a picker (human or robotic) that has a fixed position in front of the carousel. A typical vertical carousel is given in Figure1. arXiv:1404.5591v1 [math.PR] 22 Apr 2014 Carousels are widely used for storage and retrieval of small and medium-sized items, such as health and beauty products, repair parts of boilers for space heating, parts of vacuum cleaners and sewing machines, books, shoes and many other goods. In e-commerce companies use carousel to store small items and manage small individual orders. -
Queueing-Theoretic End-To-End Latency Modeling of Future Wireless Networks Schulz
Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Schulz Technische Universität Dresden Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Philipp Schulz der Fakultät Elektrotechnik und Informationstechnik der Technischen Universität Dresden zur Erlangung des akademischen Grades Doktoringenieur (Dr.-Ing.) genehmigte Dissertation Vorsitzender: Prof. Dr.-Ing. habil. Leon Urbas Gutachter: Prof. Dr.-Ing. Dr. h.c. Gerhard Fettweis Univ. Prof. Dr. techn. Markus Rupp Tag der Einreichung: 01. Oktober 2019 Tag der Verteidigung: 13. Januar 2020 Philipp Schulz Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Dissertation, 13. Januar 2020 Vodafone Chair Mobile Communications Systems Institut für Nachrichtentechnik Fakultät Elektrotechnik und Informationstechnik Technische Universität Dresden 01062 Dresden Abstract The fifth generation (5G) of mobile communication networks is envisioned to enable a variety of novel applications. These applications demand requirements from the network, which are diverse and challenging. Consequently, the mobile network has to be not only capable to meet the demands of one of these appli- cations, but also be flexible enough that it can be tailored to different needs of various services. Among these new applications, there are use cases that require low latency as well as an ultra-high reliability, e. g., to ensure unobstructed pro- duction in factory automation or road safety for (autonomous) transportation. In these domains, the requirements are crucial, since violating them may lead to financial or even human damage. Hence, an ultra-low probability of failure is necessary. Based on this, two major questions arise that are the motivation for this thesis. First, how can ultra-low failure probabilities be evaluated, since experiments or simulations would require a tremendous number of runs and, thus, turn out to be infeasible. -
On a Two-Parameter Yule-Simon Distribution
On a two-parameter Yule-Simon distribution Erich Baur ♠ Jean Bertoin ♥ Abstract We extend the classical one-parameter Yule-Simon law to a version depending on two pa- rameters, which in part appeared in [1] in the context of a preferential attachment algorithm with fading memory. By making the link to a general branching process with age-dependent reproduction rate, we study the tail-asymptotic behavior of the two-parameter Yule-Simon law, as it was already initiated in [1]. Finally, by superposing mutations to the branching process, we propose a model which leads to the full two-parameter range of the Yule-Simon law, generalizing thereby the work of Simon [20] on limiting word frequencies. Keywords: Yule-Simon model, Crump-Mode-Jagers branching process, population model with neutral mutations, heavy tail distribution, preferential attachment with fading memory. AMS subject classifications: 60J80; 60J85; 60G55; 05C85. 1 Introduction The standard Yule process Y = (Y (t))t≥0 is a basic population model in continuous time and with values in N := 1, 2,... It describes the evolution of the size of a population started { } from a single ancestor, where individuals are immortal and give birth to children at unit rate, independently one from the other. It is well-known that for every t 0, Y (t) has the geometric −t ≥ distribution with parameter e . As a consequence, if Tρ denotes an exponentially distributed random time with parameter ρ > 0 which is independent of the Yule process, then for every k N, there is the identity ∈ ∞ P(Y (T )= k)= ρ e−ρt(1 e−t)k−1e−tdt = ρB(k, ρ + 1), (1) ρ − Z0 arXiv:2001.01486v1 [math.PR] 6 Jan 2020 where B is the beta function.