Polling Systems and Their Application to Telecommunication Networks
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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. -
Product-Form in Queueing Networks
Product-form in queueing networks VRIJE UNIVERSITEIT Product-form in queueing networks ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Vrije Universiteit te Amsterdam, op gezag van de rector magnificus dr. C. Datema, hoogleraar aan de faculteit der letteren, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de faculteit der economische wetenschappen en econometrie op donderdag 21 mei 1992 te 15.30 uur in het hoofdgebouw van de universiteit, De Boelelaan 1105 door Richardus Johannes Boucherie geboren te Oost- en West-Souburg Thesis Publishers Amsterdam 1992 Promotoren: prof.dr. N.M. van Dijk prof.dr. H.C. Tijms Referenten: prof.dr. A. Hordijk prof.dr. P. Whittle Preface This monograph studies product-form distributions for queueing networks. The celebrated product-form distribution is a closed-form expression, that is an analytical formula, for the queue-length distribution at the stations of a queueing network. Based on this product-form distribution various so- lution techniques for queueing networks can be derived. For example, ag- gregation and decomposition results for product-form queueing networks yield Norton's theorem for queueing networks, and the arrival theorem implies the validity of mean value analysis for product-form queueing net- works. This monograph aims to characterize the class of queueing net- works that possess a product-form queue-length distribution. To this end, the transient behaviour of the queue-length distribution is discussed in Chapters 3 and 4, then in Chapters 5, 6 and 7 the equilibrium behaviour of the queue-length distribution is studied under the assumption that in each transition a single customer is allowed to route among the stations only, and finally, in Chapters 8, 9 and 10 the assumption that a single cus- tomer is allowed to route in a transition only is relaxed to allow customers to route in batches. -
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. -
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. -
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. -
Optimal Surplus Capacity Utilization in Polling Systems Via Fluid Models
Optimal Surplus Capacity Utilization in Polling Systems via Fluid Models Ayush Rawal, Veeraruna Kavitha and Manu K. Gupta Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai - 400076, India E-mail: ayush.rawal, vkavitha, manu.gupta @iitb.ac.in Abstract—We discuss the idea of differential fairness in polling while maintaining the QoS requirements of primary customers. systems. One such example scenario is: primary customers One can model this scenario with polling systems, when it demand certain Quality of Service (QoS) and the idea is to takes non-zero time to switch the services between the two utilize the surplus server capacity to serve a secondary class of customers. We use achievable region approach for this. Towards classes of customers. Another example scenario is that of data this, we consider a two queue polling system and study its and voice users utilizing the same wireless network. In this ‘approximate achievable region’ using a new class of delay case, the network needs to maintain the drop probability of priority kind of schedulers. We obtain this approximate region, the impatient voice customers (who drop calls if not picked-up via a limit polling system with fluid queues. The approximation is within a negligible waiting times) below an acceptable level. accurate in the limit when the arrival rates and the service rates converge towards infinity while maintaining the load factor and Alongside, it also needs to optimize the expected sojourn times the ratio of arrival rates fixed. We show that the set of proposed of the data calls. schedulers and the exhaustive schedulers form a complete class: Polling systems can be categorized on the basis of different every point in the region is achieved by one of those schedulers. -
A Bitcoin-Inspired Infinite-Server Model with a Random Fluid Limit
A BITCOIN-INSPIRED INFINITE-SERVER MODEL WITH A RANDOM FLUID LIMIT MARIA FROLKOVA & MICHEL MANDJES Abstract. The synchronization process inherent to the Bitcoin network gives rise to an infinite- server model with the unusual feature that customers interact. Among the closed-form character- istics that we derive for this model is the busy period distribution which, counterintuitively, does not depend on the arrival rate. We explain this by exploiting the equivalence between two specific service disciplines, which is also used to derive the model's stationary distribution. Next to these closed-form results, the second major contribution concerns an asymptotic result: a fluid limit in the presence of service delays. Since fluid limits arise under scalings of the law-of-large-numbers type, they are usually deterministic, but in the setting of the model discussed in this paper the fluid limit is random (more specifically, of growth-collapse type). Key words. Stochastic-process limit ◦ fluid limit ◦ growth-collapse process ◦ Bitcoin. Affiliations. M. Frolkova and M. Mandjes are with Korteweg-de Vries Institute for Mathemat- ics, University of Amsterdam, Science Park 105-107, 1098 XG Amsterdam, the Netherlands. M. Frolkova is corresponding author (email: [email protected]). 1. Introduction The Bitcoin network is a payment system where all transactions between participants are carried out in the digital Bitcoin currency. These transactions are stored in a database called blockchain. In fact, every Bitcoin user maintains a blockchain version, thus keeping track of the global Bitcoin transaction history. This ensures transparency | one of the advertized Bitcoin features. Informa- tion spreads across the network via peer-to-peer communication. -
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. -
3 Lecture 3: Queueing Networks and Their Fluid and Diffusion Approximation
3 Lecture 3: Queueing Networks and their Fluid and Diffusion Approximation • Fluid and diffusion scales • Queueing networks • Fluid and diffusion approximation of Generalized Jackson networks. • Reflected Brownian Motions • Routing in single class queueing networks. • Multi class queueing networks • Stability of policies for MCQN via fluid • Optimization of MCQN — MDP and the fluid model. • Formulation of the fluid problem for a MCQN 3.1 Fluid and diffusion scales As we saw, queues can only be studied under some very detailed assumptions. If we are willing to assume Poisson arrivals and exponetial service we get a very complete description of the queuing process, in which we can study almost every phenomenon of interest by analytic means. However, much of what we see in M/M/1 simply does not describe what goes on in non M/M/1 systems. If we give up the assumption of exponential service time, then it needs to be replaced by a distribution function G. Fortunately, M/G/1 can also be analyzed in moderate detail, and quite a lot can be said about it which does not depend too much on the detailed form of G. In particular, for the M/G/1 system in steady state, we can calculate moments of queue length and waiting times in terms of the same moments plus one of the distribution G, and so we can evaluate performance of M/G/1 without too many details on G, sometimes 2 E(G) and cG suffice. Again, behavior of M/G/1 does not describe what goes on in GI/G/1 systems. -
Applications of Markov Decision Processes in Communication Networks: a Survey
Applications of Markov Decision Processes in Communication Networks: a Survey Eitan Altman∗ Abstract We present in this Chapter a survey on applications of MDPs to com- munication networks. We survey both the different applications areas in communication networks as well as the theoretical tools that have been developed to model and to solve the resulting control problems. 1 Introduction Various traditional communication networks have long coexisted providing dis- joint specific services: telephony, data networks and cable TV. Their operation has involved decision making that can be modelled within the stochastic control framework. Their decisions include the choice of routes (for example, if a direct route is not available then a decision has to be taken which alternative route can be taken) and call admission control; if a direct route is not available, it might be wise at some situations not to admit a call even if some alternative route exists. In contrast to these traditional networks, dedicated to a single application, today’s networks are designed to integrate heterogeneous traffic types (voice, video, data) into one single network. As a result, new challenging control prob- lems arise, such as congestion and flow control and dynamic bandwidth alloca- tion. Moreover, control problems that had already appeared in traditional net- works reappear here with a higher complexity. For example, calls corresponding to different applications require typically different amount of network resources (e.g., bandwidth) and different performance bounds (delays, loss probabilities, throughputs). Admission control then becomes much more complex than it was in telephony, in which all calls required the same performance characteristics and the same type of resources (same throughput, bounds on loss rates and on delay variation). -
FLUID and DIFFUSION LIMITS for QUEUES in SLOWLY CHANGING ENVIRONMENTS G. L. CHOUDHURY A. MANDELBAUM AT&T Laboratories Facult
FLUID AND DIFFUSION LIMITS FOR QUEUES IN SLOWLY CHANGING ENVIRONMENTS G. L. CHOUDHURY A. MANDELBAUM AT&T Laboratories Faculty of Industrial and Room 1L-238 Management Engineering Holmdel, NJ 07730-3030 The Technion, Haifa 32000 ISRAEL M. I. REIMAN W. WHITT Bell Labs AT&T Laboratories Lucent Technologies Room 2C-178 Room 2C-117 Murray Hill, NJ 07974-0636 Murray Hill, NJ 07974-0636 Stochastic Models 13 (1997) 121{146 (Received the first Marcel F. Neuts best paper award) Key Words: queues in random environments, nearly-completely-decomposable Markov chains, Markov-modulated process, piecewise-stationary Mt=Gt=1 queue, stochastic fluid models, diffusion processes, fluid limit, heavy traffic. ABSTRACT We consider an infinite-capacity s-server queue in a finite-state random envi- ronment, where the traffic intensity exceeds 1 in some environment states and the environment states change slowly relative to arrivals and service comple- tions. Queues grow in unstable environment states, so that it is useful to look at the system in the time scale of mean environment-state sojourn times. As the mean environment-state sojourn times grow, the queue-length and work- load processes grow. However, with appropriate normalizations, these pro- cesses converge to fluid processes and diffusion processes. The diffusion process in a random environment is a refinement of the fluid process in a random envi- ronment. We show how the scaling in these limits can help explain numerical results for queues in slowly changing random environments. For that purpose, we apply recently developed numerical-transform-inversion algorithms for the MAP/G/1 queue and the piecewise-stationary Mt=Gt=1 queue. -
A Functional Central Limit Theorem for the M/GI/ Queue
The Annals of Applied Probability 2008, Vol. 18, No. 6, 2156–2178 DOI: 10.1214/08-AAP518 c Institute of Mathematical Statistics, 2008 A FUNCTIONAL CENTRAL LIMIT THEOREM FOR THE M/GI/∞ QUEUE By Laurent Decreusefond and Pascal Moyal GET, T´el´ecom Paris, and Universit´eParis, Dauphine and Universit´ede Technologie de Compi`egne In this paper, we present a functional fluid limit theorem and a functional central limit theorem for a queue with an infinity of servers M/GI/∞. The system is represented by a point-measure val- ued process keeping track of the remaining processing times of the customers in service. The convergence in law of a sequence of such processes after rescaling is proved by compactness-uniqueness meth- ods, and the deterministic fluid limit is the solution of an integrated equation in the space S′ of tempered distributions. We then establish the corresponding central limit theorem, that is, the approximation of the normalized error process by a S′-valued diffusion. We apply these results to provide fluid limits and diffusion approximations for some performance processes. 1. Introduction. The queues with an infinite reservoir of servers are clas- sical models in queueing theory. In such cases, all the customers are imme- diately taken care of upon arrival, and spend in the system a sojourn time equal to their service time. Beyond its interest to represent telecommunica- tion networks or computers architectures in which the number of resources is extremely large, this model (commonly referred to as pure delay queue) has been often used for comparison to other ones whose dynamics are formally much more complicated, but close in some sense.