Product-Form in Queueing Networks
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Slides As Printed
Computer Systems Modelling Ian Leslie Notes by Dr R.J. Gibbens (minor edits by Ian Leslie) Computer Laboratory University of Cambridge Computer Science Tripos, Part II Lent Term 2016/17 CSM 2016/17 (1) Course overview 12 lectures covering: ◮ Introduction to modelling: ◮ what is it and why is it useful? ◮ Simulation techniques: ◮ random number generation, ◮ Monte Carlo simulation techniques, ◮ statistical analysis of results from simulation and measurements; ◮ Queueing theory: ◮ applications of Markov Chains, ◮ single/multiple servers, ◮ queues with finite/infinite buffers, ◮ queueing networks. CSM 2016/17 (2) Recommended books Ross, S.M. Probability Models for Computer Science Academic Press, 2002 Mitzenmacher, M & Upfal, E. Probability and computing: randomized algorithms and probabilistic analysis Cambridge University Press, 2005 Jain, A.R. The Art of Computer Systems Performance Analysis Wiley, 1991 Kleinrock, L. Queueing Systems — Volume 1: Theory Wiley, 1975 CSM 2016/17 (3) Introduction to modelling CSM 2016/17 (4) Why model? ◮ A manufacturer may have a range of compatible systems with different characteristics — which configuration would be best for a particular application? ◮ A system is performing poorly — what should be done to improve it? Which problems should be tackled first? ◮ Fundamental design decisions may affect performance of a system. A model can be used as part of the design process to avoid bad decisions and to help quantify a cost/benefit analysis. CSM 2016/17 (5) A simple queueing system arrival queue server departure ◮ -
The Queueing Network Analyzer
THE BELL SYSTEM TECHNICAL JOURNAL Vol. 62, No.9, November 1983 Printed in U.S.A. The Queueing Network Analyzer By W. WHITT* (Manuscript received March 11, 1983) This paper describes the Queueing Network Analyzer (QNA), a software package developed at Bell Laboratories to calculate approximate congestion measures for a network of queues. The first version of QNA analyzes open networks of multiserver nodes with the first-come, first-served discipline and no capacity constraints. An important feature is that the external arrival processes need not be Poisson and the service-time distributions need not be exponential. Treating other kinds of variability is important. For example, with packet-switchedcommunication networks we need to describe the conges tion resulting from bursty traffic and the nearly constant service times of packets. The general approach in QNA is to approximately characterize the arrival processes by two or three parameters and then analyze the individual nodes separately. The first version of QNA uses two parameters to characterize the arrival processes and service times, one to describe the rate and the other to describe the variability. The nodes are then analyzed as standard GI/G/m queues partially characterized by the first two moments of the interarrival time and service-time distributions. Congestion measures for the network as a whole are obtained by assuming as an approximation that the nodes are stochastically independent given the approximate flow parameters. I. INTRODUCTION AND SUMMARY Networks of queues have proven to be useful models to analyze the performance of complex systems such as computers, switching ma chines, communications networks, and production job shOpS.1-7 To facilitate the analysis of these models, several software packages have * Bell Laboratories. -
The MVA Priority Approximation
The MVA Priority Approximation RAYMOND M. BRYANT and ANTHONY E. KRZESINSKI IBM Thomas J. Watson Research Center and M. SEETHA LAKSHMI and K. MANI CHANDY University of Texas at Austin A Mean Value Analysis (MVA) approximation is presented for computing the average performance measures of closed-, open-, and mixed-type multiclass queuing networks containing Preemptive Resume (PR) and nonpreemptive Head-Of-Line (HOL) priority service centers. The approximation has essentially the same storage and computational requirements as MVA, thus allowing computa- tionally efficient solutions of large priority queuing networks. The accuracy of the MVA approxima- tion is systematically investigated and presented. It is shown that the approximation can compute the average performance measures of priority networks to within an accuracy of 5 percent for a large range of network parameter values. Accuracy of the method is shown to be superior to that of Sevcik's shadow approximation. Categories and Subject Descriptors: D.4.4 [Operating Systems]: Communications Management-- network communication; D.4.8 [Operating Systems]: Performance--modeling and prediction; queuing theory General Terms: Performance, Theory Additional Key Words and Phrases: Approximate solutions, error analysis, mean value analysis, multiclass queuing networks, priority queuing networks, product form solutions 1. INTRODUCTION Multiclass queuing networks with product-form solutions [3] are widely used to model the performance of computer systems and computer communication net- works [11]. The effective application of these models is largely due to the efficient computational methods [5, 9, 13, 18, 21] that have been developed for the solution of product-form queuing networks. However, many interesting and significant system characteristics cannot be modeled by product-form networks. -
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. -
Introduction to Queueing Theory Review on Poisson Process
Contents ELL 785–Computer Communication Networks Motivations Lecture 3 Discrete-time Markov processes Introduction to Queueing theory Review on Poisson process Continuous-time Markov processes Queueing systems 3-1 3-2 Circuit switching networks - I Circuit switching networks - II Traffic fluctuates as calls initiated & terminated Fluctuation in Trunk Occupancy Telephone calls come and go • Number of busy trunks People activity follow patterns: Mid-morning & mid-afternoon at All trunks busy, new call requests blocked • office, Evening at home, Summer vacation, etc. Outlier Days are extra busy (Mother’s Day, Christmas, ...), • disasters & other events cause surges in traffic Providing resources so Call requests always met is too expensive 1 active • Call requests met most of the time cost-effective 2 active • 3 active Switches concentrate traffic onto shared trunks: blocking of requests 4 active active will occur from time to time 5 active Trunk number Trunk 6 active active 7 active active Many Fewer lines trunks – minimize the number of trunks subject to a blocking probability 3-3 3-4 Packet switching networks - I Packet switching networks - II Statistical multiplexing Fluctuations in Packets in the System Dedicated lines involve not waiting for other users, but lines are • used inefficiently when user traffic is bursty (a) Dedicated lines A1 A2 Shared lines concentrate packets into shared line; packets buffered • (delayed) when line is not immediately available B1 B2 C1 C2 (a) Dedicated lines A1 A2 B1 B2 (b) Shared line A1 C1 B1 A2 B2 C2 C1 C2 A (b) -
EUROPEAN CONFERENCE on QUEUEING THEORY 2016 Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop
EUROPEAN CONFERENCE ON QUEUEING THEORY 2016 Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop To cite this version: Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop. EUROPEAN CONFERENCE ON QUEUEING THEORY 2016. Jul 2016, Toulouse, France. 72p, 2016. hal- 01368218 HAL Id: hal-01368218 https://hal.archives-ouvertes.fr/hal-01368218 Submitted on 19 Sep 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. EUROPEAN CONFERENCE ON QUEUEING THEORY 2016 Toulouse July 18 – 20, 2016 Booklet edited by Urtzi Ayesta LAAS-CNRS, France Marko Boon Eindhoven University of Technology, The Netherlands‘ Balakrishna Prabhu LAAS-CNRS, France Rhonda Righter UC Berkeley, USA Maaike Verloop IRIT-CNRS, France 2 Contents 1 Welcome Address 4 2 Organization 5 3 Sponsors 7 4 Program at a Glance 8 5 Plenaries 11 6 Takács Award 13 7 Social Events 15 8 Sessions 16 9 Abstracts 24 10 Author Index 71 3 1 Welcome Address Dear Participant, It is our pleasure to welcome you to the second edition of the European Conference on Queueing Theory (ECQT) to be held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT in Toulouse. -
Queueing Theory
Queueing theory Agner Krarup Erlang, a Danish engineer who worked for the Copenhagen Telephone Exchange, published the first paper on what would now be called queueing theory in 1909.[8][9][10] He modeled the number of telephone calls arriving at an exchange by a Poisson process and solved the M/D/1 queue in 1917 and M/D/k queueing model in 1920.[11] In Kendall’s notation: • M stands for Markov or memoryless and means ar- rivals occur according to a Poisson process • D stands for deterministic and means jobs arriving at the queue require a fixed amount of service • k describes the number of servers at the queueing node (k = 1, 2,...). If there are more jobs at the node than there are servers then jobs will queue and wait for service Queue networks are systems in which single queues are connected The M/M/1 queue is a simple model where a single server by a routing network. In this image servers are represented by serves jobs that arrive according to a Poisson process and circles, queues by a series of retangles and the routing network have exponentially distributed service requirements. In by arrows. In the study of queue networks one typically tries to an M/G/1 queue the G stands for general and indicates obtain the equilibrium distribution of the network, although in an arbitrary probability distribution. The M/G/1 model many applications the study of the transient state is fundamental. was solved by Felix Pollaczek in 1930,[12] a solution later recast in probabilistic terms by Aleksandr Khinchin and Queueing theory is the mathematical study of waiting now known as the Pollaczek–Khinchine formula.[11] lines, or queues.[1] In queueing theory a model is con- structed so that queue lengths and waiting time can be After World War II queueing theory became an area of [11] predicted.[1] Queueing theory is generally considered a research interest to mathematicians. -
Delay Models in Data Networks
3 Delay Models in Data Networks 3.1 INTRODUCTION One of the most important perfonnance measures of a data network is the average delay required to deliver a packet from origin to destination. Furthennore, delay considerations strongly influence the choice and perfonnance of network algorithms, such as routing and flow control. For these reasons, it is important to understand the nature and mechanism of delay, and the manner in which it depends on the characteristics of the network. Queueing theory is the primary methodological framework for analyzing network delay. Its use often requires simplifying assumptions since, unfortunately, more real- istic assumptions make meaningful analysis extremely difficult. For this reason, it is sometimes impossible to obtain accurate quantitative delay predictions on the basis of queueing models. Nevertheless, these models often provide a basis for adequate delay approximations, as well as valuable qualitative results and worthwhile insights. In what follows, we will mostly focus on packet delay within the communication subnet (i.e., the network layer). This delay is the sum of delays on each subnet link traversed by the packet. Each link delay in tum consists of four components. 149 150 Delay Models in Data Networks Chap. 3 1. The processinR delay between the time the packet is correctly received at the head node of the link and the time the packet is assigned to an outgoing link queue for transmission. (In some systems, we must add to this delay some additional processing time at the DLC and physical layers.) 2. The queueinR delay between the time the packet is assigned to a queue for trans- mission and the time it starts being transmitted. -
Matrix Geometric Approach for Random Walks
Matrix geometric approach for random walks Citation for published version (APA): Kapodistria, S., & Palmowski, Z. B. (2017). Matrix geometric approach for random walks: stability condition and equilibrium distribution. Stochastic Models, 33(4), 572-597. https://doi.org/10.1080/15326349.2017.1359096 Document license: CC BY-NC-ND DOI: 10.1080/15326349.2017.1359096 Document status and date: Published: 02/10/2017 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion
Communications and Network, 2015, 7, 21-29 Published Online February 2015 in SciRes. http://www.scirp.org/journal/cn http://dx.doi.org/10.4236/cn.2015.71003 Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion Faisal Shahzad1, Muhammad Faheem Mushtaq1, Saleem Ullah1*, M. Abubakar Siddique2, Shahzada Khurram1, Najia Saher1 1Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur, Pakistan 2College of Computer Science, Chongqing University, Chongqing, China Email: [email protected], [email protected], *[email protected], [email protected], [email protected], [email protected] Received 12 June 2014; accepted 30 January 2015; published 2 February 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more in- teresting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (β) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum net- work throughput for β ≥ 0.7 remains consistent in a single server case, while in multi-server case for β ≥ 0.5 throughput surpass the Marko chain queuing system. -
BUSF 40901-1/CMSC 34901-1: Stochastic Performance Modeling Winter 2014
BUSF 40901-1/CMSC 34901-1: Stochastic Performance Modeling Winter 2014 Syllabus (January 15, 2014) Instructor: Varun Gupta Office: 331 Harper Center e-mail: [email protected] Phone: 773-702-7315 Office hours: by appointment Class Times: Wed, Fri { 10:10-11:30 am { Harper Center (3A) Final Exam (tentative): March 21, Friday { 8:00-11:00am { Harper Center (3A) Course Website: http://chalk.uchicago.edu Course Objectives This is an introductory course in queueing theory and performance modeling, with applications including but not limited to service operations (healthcare, call centers) and computer system resource management (from datacenter to kernel level). The aim of the course is two-fold: 1. Build insights into best practices for designing service systems (How many service stations should I provision? What speed? How should I separate/prioritize customers based on their service requirements?) 2. Build a basic toolbox for analyzing queueing systems in particular and stochastic processes in general. Tentative list of topics: Open/closed queueing networks; Operational laws; M=M=1 queue; Burke's theorem and reversibility; M=M=k queue; M=G=1 queue; G=M=1 queue; P h=P h=k queues and their solution using matrix-analytic methods; Arrival theorem and Mean Value Analysis; Analysis of scheduling policies (e.g., Last-Come-First Served; Processor Sharing); Jackson network and the BCMP theorem (product form networks); Asymptotic analysis (M=M=k queue in heavy/light traf- fic, Supermarket model in mean-field regime) Prerequisites Exposure to undergraduate probability (random variables, discrete and continuous probability dis- tributions, discrete time Markov chains) and calculus is required. -
Queing Theory
QUEUING THEORY Jaroslav Sklenar Department of Statistics and Operations Research University of Malta Contents Introduction 1 Elements of Queuing Systems 2 Kendall Classification 3 Birth and Death Processes 4 Poisson Process 4.1 Merging and Splitting Poisson Processes 4.2 Model M/G/ 5 Model M/M/1 5.1 PASTA 6 General Results 6.1 Markovian Systems with State Dependent Rates 7 Model with Limited Capacity 8 Multichannel Model 9 Model with Limited Population 9.1 Model with Spares 10 Other Markovian Models 10.1 Multichannel with Limited Capacity and Limited Population 10.2 Multichannel with Limited Capacity and Unlimited Population 10.3 Erlang’s Loss Formula 10.4 Model with Unlimited Service 11 Model M/G/1 12 Simple Non-Markovian Models 13 Queuing Networks 13.1 Feed Forward Networks 13.2 Open Jackson Networks 13.3 Closed Jackson Networks 14 Selected Advanced Models 14.1 State Dependent Rates Generalized 14.2 Impatience 14.3 Bulk Input 14.4 Bulk Service 14.5 Systems with Priorities 14.6 Model G/M/1 15 Other Results and Methods References Appendices Introduction This text is supposed to be used in courses oriented to basic theoretical background and practical application of most important results of Queuing Theory. The assumptions are always clearly explained, but some proofs and derivations are either presented in appendices or skipped. After reading this text, the reader is supposed to understand the basic ideas, and to be able to apply the theoretical results in solving practical problems. Symbols used in this text are unified with the book [1].