
Queueing Theory 2
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This second volume includes eight chapters written by experts wellknown in their areas. The book conducts a stability analysis of certain types of multiserver regenerative queueing systems; a transient evaluation of Markovian queueing systems, focusing on closed-form distributions and numerical techniques; analysis of queueing models in service sectors using analytical and simulation approaches; plus an investigation of probability distributions in queueing models and their use in economics, industry, demography and environmental studies.
This book also considers techniques for the control of information in queueing systems and their impact on strategic customer behavior, social welfare and the revenue of monopolists. In addition, applications of maximum entropy methods of inference for the analysis of a stable M/G/1 queue with heavy tails, and inventory models with positive service time - including perishable items and stock supplied using various algorithmic control policies ((s; S); (r;Q), etc.).
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Vladimir Anisimov is Full Professor in Applied Statistics. He works in the Center for Design & Analysis at Amgen Inc. in London, UK. His research interests include probability models and stochastic processes, clinical trials modeling, applied statistics, queueing models and asymptotic techniques.
Nikolaos Limnios is Full Professor in Applied Mathematics at the University of Technology of Compiegne, part of the Sorbonne University Group, in France. His research interests include stochastic processes and statistics, Markov and semi-Markov processes, random evolutions with applications in reliability, queueing systems, earthquakes and biology.
Content
Preface xi
Chapter 1. Stability Analysis of Queueing Systems based on Synchronization of the Input and Majorizing Output Flows 1 Larisa AFANASEVA
1.1. Introduction 1
1.2. Model description 4
1.3. Auxiliary service process 6
1.4. Instability result for the case ¿ = 1 9
1.5. Stochastic boundedness for the case ¿ < 1 10
1.6. Queueing system with unreliable servers and preemptive resume service discipline 11
1.7. Discrete-time queueing system with interruptions and preemptive repeat different service discipline 15
1.8. Queueing system with a preemptive priority discipline 18
1.9. Queueing system with simultaneous service of a customer by a random number of servers 20
1.10. Applications to transport systems analysis 23
1.11. Conclusion 28
1.12. Acknowledgment 29
1.13. References 29
Chapter 2. Queueing Models in Services - Analytical and Simulation Approach 33 Srinivas R. CHAKRAVARTHY
2.1. Introduction 33
2.2. Phase-type distributions and the batch Markovian arrival process 34
2.2.1. Phase-type distributions 35
2.2.2. Some useful results related to continuous PH distributions 36
2.2.3. The batch Markovian arrival process 40
2.3. Generation of MAP processes for numerical purposes 42
2.4. Analysis of selected queueing models of BMAP/G/c type 44
2.4.1. MAP/PH/1 queueing model 44
2.4.2. The system performance measures 48
2.4.3. Illustrative numerical examples for MAP/PH/1 49
2.4.4. MAP/M/c queueing model 55
2.4.5. The system performance measures 57
2.4.6. Illustrative numerical examples for MAP/M/c 57
2.5. Simulated models of BMAP/G/c type queues 58
2.5.1. Simulated model validation using MAP/M/c type queues 59
2.5.2. Simulated model validation using MAP/PH/1 type queues 59
2.5.3. Selected simulated models of BMAP/G/c type queues 59
2.6. Analysis of selected queueing models of BMAP/G/c type with a vacation 66
2.6.1. MAP/PH/1 queueing model with a vacation 66
2.6.2. The system performance measures 69
2.6.3. Illustrative numerical examples for MAP/PH/1 with a vacation 69
2.6.4. Validation of the simulated model for vacation type queues 74
2.6.5. Selected simulated models of BMAP/G/c type queues with a vacation 75
2.7. Acknowledgment 78
2.8. References 78
Chapter 3. Distributions and Random Processes Related to Queueing and Reliability Models 81 Boyan DIMITROV
3.1. Some useful notations, relationships and interpretations 81
3.2. Unreliable service model and reliability maintenance 85
3.3. Characterizations of exponential and geometric distributions via properties of service times 88
3.3.1. Instant repairs: characterization of geometric distribution 89
3.3.2. Instant repairs: characterizations of the exponential distribution 94
3.3.3. Various simplifying conditions 101
3.3.4. Unreliable service, repair times included 111
3.4. Probability distributions almost having lack of memory property 115
3.4.1. Service time on an unreliable server: instantaneous repairs 116
3.4.2. Properties of ALM distributions, and equivalent presentations 119
3.4.3. Periodicity in natural phenomena 126
3.5. Random processes with a periodic nature 126
3.5.1. Counting processes 127
3.5.2. Characterization of an NPP 128
3.5.3. Applications in risk modeling 131
3.6. Conclusions 132
3.7. References 133
Chapter 4. The Impact of Information Structure on Strategic Behavior in Queueing Systems 137 Antonis ECONOMOU
4.1. Introduction 138
4.2. Game-theoretical framework in queueing 139
4.3. The unobservable model 142
4.4. The observable model 146
4.5. Comparison of the unobservable and the observable models 151
4.6. Partially observable models 153
4.7. Heterogeneously observable models 158
4.8. Observable-with-delay models 162
4.9. Conclusions and literature review for further study 167
4.10. Acknowledgments 167
4.11. References 168
Chapter 5. Non-extensive Maximum Entropy Formalisms and Inductive Inference of a Stable M/G/1 Queue with Heavy Tails 171 Demetres D. KOUVATSOS and Ismail A. MAGEED
5.1. Introduction 172
5.2. General systems and inductive ME formalisms 175
5.2.1. "Classical" Shannon's EME formalism with short-range interactions 175
5.2.2. Rényi's and Tsallis's NME formalisms with long-range interactions 176
5.3. NME formalisms and EME consistency axioms 177
5.4. A stable M/G/1 queue with long-range interactions 179
5.4.1. Background: Shannon's EME state probability of a stable M/G/1 queue 179
5.4.2. Tsallis' and Rényi's NME state probabilities of a stable M/G/1 queue 180
5.4.3. Exact Rényi's and Tsallis' NME state probabilities with distinct GEq-type service time distributions 183
5.5. Numerical experiments and interpretations 188
5.6. Conclusions 195
5.7. Acknowledgments 196
5.8. Appendix: Rényi's NME formalisms versus EME consistency axioms 196
5.8.1. Uniqueness 196
5.8.2. Invariance 197
5.8.3. System independence 197
5.8.4. Subset independence 198
5.9. References 199
Chapter 6. Inventory with Positive Service Time: a Survey 201 Achyutha KRISHNAMOORTHY, Dhanya SHAJIN and Viswanath C. NARAYANAN
6.1. Introduction 201
6.2. Queueing inventory models 203
6.2.1. Single-commodity queueing-inventory systems 206
6.2.2. Production inventory systems 215
6.2.3. Multicommodity queueing-inventory system 217
6.2.4. Retrial queues with inventory 219
6.2.5. Queues requiring additional items for service 222
6.2.6. Queueing-inventory: some work in progress and suggestions for future studies 225
6.3. Acknowledgment 227
6.4. References 227
Chapter 7. A Stability Analysis Method of Regenerative Queueing Systems 239 Evsey MOROZOV and Bart STEYAERT
7.1. Introduction 239
7.2. Preliminaries 241
7.3. The single-server system 244
7.4. The zero-delayed multiserver system 248
7.5. The delayed multiserver system: finiteness of the first regeneration period 252
7.6. Instability 256
7.6.1. Some comments on the method 260
7.7. Related research 262
7.8. Acknowledgments 266
7.9. References 266
Chapter 8. Transient Analysis of Markovian Queueing Systems: a Survey with Focus on Closed-forms and Uniformization 269 Gerardo RUBINO
8.1. Introduction 270
8.2. Basics on Markovian queues 272
8.2.1. Markov models 272
8.2.2. Uniformization 273
8.3. First examples 275
8.3.1. The Ehrenfest model in continuous-time 275
8.3.2. The M/M/8 model 276
8.3.3. A queue with no server and catastrophes 277
8.3.4. The fundamental M/M/1 model 278
8.3.5. M/M/1 with bounded waiting room: the M/M/1/H model 282
8.3.6. Comments 284
8.4. An uniformization-based path for the M/M/1 with matrix generating functions 284
8.4.1. General case 286
8.4.2. Mean number of customers at time t in the M/M/1 287
8.5. An uniformization-based path using duality 290
8.5.1. Duality 290
8.5.2. The path toward the transient state distributions using duality 293
8.5.3. Application to the M/M/1 queueing system 294
8.5.4. Application to the M/M/1/H queueing system 295
8.5.5. Application to an M/M/1/H model with catastrophes 297
8.6. Other transient results 299
8.6.1. Busy period of the M/M/1 299
8.6.2. Max backlog of the M/M/1 over a finite time interval 299
8.6.3. M/E/1 300
8.7. Conclusions 302
8.8. References 302
List of Authors 307
Index 309
1
Stability Analysis of Queueing Systems based on Synchronization of the Input and Majorizing Output Flows
Larisa AFANASEVA
Department of Probability, Faculty of Mathematics and Mechanics, Lomonosov Moscow State University, Russia
This chapter is focused on the stability conditions for a multiserver queueing system with heterogeneous servers and a regenerative input flow X. The main idea is constructing an auxiliary service process Y, which is also a regenerative flow and determination of the common points of regeneration for both processes X and Y. Then the traffic rate is defined in terms of the mean of the increments of these processes on a common regeneration period. It allows us to use well-known results from the renewal theory to find the instability and stability conditions. The possibilities of the proposed approach are demonstrated by examples. We also present the applications to transport system capacity analysis.
1.1. Introduction
This chapter deals with the study of stability conditions for a heterogeneous multiserver queueing system with regenerative input flow (Afanaseva 2019).
We consider queueing systems with regenerative input flow for three reasons. First, a process describing the performance of the system under some natural conditions turns out to be a classical regenerative process (Asmussen 2003; Thorisson 2000) and the renewal theory gives very effective tools for asymptotic analysis of the system. Second, the class of regenerative flows is rather wide and includes fundamental flows from queueing theory. Finally, regenerative flow has some useful properties that allow us to investigate various applied models (Afanasyeva and Bashtova 2014).
The main objective of our study is to determine conditions under which the process describing the performance of the system is stochastically bounded and therefore, under some additional assumptions, a stable process.
Let us note that stability results for the classical homogeneous multiserver queue are very well known. We refer to the works of (Kiefer and Wolfowitz 1955) for the GI|GI|m system and the general ergodicity results of (Loynes 1962). As it was shown in Sadowsky (1995), the proposed approach could not be applied for heterogeneous systems. Instead, in the work of (Sadowsky 1995), the stability is examined from the point of view of Harris recurrent Markov chain theory. Based on the works of (Malyshev and Menshikov 1982; Meyn and Tweedie 2009; Georgiadis and Szpankowski 1992; Szpankowski 1994), the author obtained two results concerning the irreducibility and positive Harris recurrence of the corresponding process.
The heterogeneous multiserver queueing system with regenerative flow was investigated in the papers of (Afanasyeva and Tkachenko 2014, 2016, 2018). Under some assumptions, the necessary and sufficient stability condition was obtained there.
We consider m-server queueing system S with regenerative input flow X with intensity ?X. The main assumption is that the process X does not depend on the stochastic sequences and processes determining the work of the servers, such as service time, the moments of breakdowns and recovery of the servers, and the random numbers of the servers required for the service of the customers.
We construct an auxiliary system S0 with input flow X0. The latter does not depend on X and is determined by the stochastic sequence describing the work of the servers. The flow X0 is constructed in such a way that there are always customers for service. We introduce an auxiliary flow Y, which is the number of customers served in the system S0 up to time t. Then we establish the conditions of Y being the regenerative flow with the intensity ??.
The basic idea is constructing the common points of regeneration for both processes X and Y. Then we define the traffic rate of the system in terms of the first moments of increments of these processes on the common regeneration period. Under some conditions, we estimate the increments of the service process ?(t) in the original system S (that is the number of customers served in the system up to time t) with the help of increments of an auxiliary process Y (t). It allows us to prove instability results and to find conditions of stochastic boundedness of the number of customers Q(t) at the system at time t as t 8.
One more traditional approach to the analysis of stability of the queueing systems is based on the regenerative structure of the processes describing the states of the system, such as the number of customers in the system or workload process. We make assumptions providing the presence of the points of the regeneration for the processes. Then, it is required to establish the finiteness of the mean of the times between the regenerations in accordance with the theorem of (Smith 1955). It is quite a complex problem for non-Markovian processes. Exactly this approach is employed in several works of Morozov and colleagues (Morozov et al. 2011; Morozov 2004, 2007; Morozov and Dimitriou 2017; Morozov and Rumyantsev 2016) for establishing the conditions for stability of a wide circle of models. Although our approach is also based on the notions of regeneration and synchronization, it significantly differs from the approach employed in the works mentioned earlier. Our main objective is to establish necessary and sufficient conditions of the stochastic boundedness of the process, describing the system. Thus, this process does not have to be regenerative. Let us point out that a similar situation emerges in the classic models (Borovkov 1976). The analysis of the stochastic boundedness of the process is important for the applications, since precisely the existence of the stochastic boundaries manifests that the system successfully deals with the task. Our conditions may seem too restrictive to be useful in applications. Therefore, we consider four models with service interruptions as examples. In particular, in section 1.7, a discrete-time heterogeneous multiserver queueing system with a regenerative input flow and service interruptions, which are described by independent renewal processes for various servers, is discussed. It is shown that the traffic rate ? is not expressed in terms of the first moments of the random variables defining the model for the preemptive repeat different service discipline (Gaver 1962). We also prove that Q(t) 8 as t 8 when ? = 1 and Q(t) is a stochastically bounded process when ? < 1.
We should consider that models with service interruptions occur in numerous applications including manufacturing processes, multiprocessor computer networks, telecommunicating networks and various types of service counters.
White and Christie (1958) were the first to study queueing systems with interruptions. Those authors investigated the M|M|1 model with preemptive resume priority discipline. Their results were later extended by (Avi-Itzhak and Naor 1963) and (Thiruvengadam 1963) who study queues with general service times. Gaver (1962) studied single-server queues with batch Poisson arrivals and generally distributed service times. So far there is extensive literature concerning queueing systems with interruptions. There are some review papers that cover most of the literature in this sphere. Some of the important papers on the single-server case are presented in Fiems and Bruneel (2013).
The most extensive literature survey on systems with interruptions both for single-server and multiserver cases is given by (Krishnamoorthy et al. 2012). This paper also covers some non-Markovian multichannel systems with homogeneous servers. There are some other articles with extensive literature survey as well (Pechinkin et al. 2009; Morozov et al. 2011). Nevertheless, to the best of our knowledge, there are no papers that study the stability problem for multichannel queueing systems with heterogeneous servers in non-Markovian case with general input flow and service times. Synchronization method combined with the regenerative theory is one of the powerful approaches to obtain stability conditions for such systems.
Let us also mention the fluid approximation approach as an alternative to the synchronization approach followed here. Such an approach has lent to significant progress in stability analysis of multiclass queueing networks (Dai 1995; Chen 1995; Chen and Yao 2001). See also Foss and Konstantopoulos (2004) for a survey of various approaches to stability of queueing systems with a focus on the fluid approach. Nevertheless, our analysis does not rely on a fluid approach because to the best of our knowledge, the synchronization method with regard to regenerative structure of the processes turns out to be suitable for obtaining complete and transparent proofs as well as natural stability conditions for the model at hand.
We also note that stability analysis is an essential and challenging stage of investigation of a stochastic model, however, stability conditions may be of independent interest. In particular, the stability criterion of the multiserver model can be used for the capacity planning of the model at the design stage to obtain the lower bound of the capacity that keeps system stable. As an example of applications of our results, we estimate the carrying capacity of the automobile road, intersected by a...
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