Atjaunināt sīkdatņu piekrišanu

E-grāmata: Network Traffic Engineering - Stochastic Models and Applications: Stochastic Models and Applications [Wiley Online]

  • Formāts: 816 pages
  • Izdošanas datums: 02-Oct-2020
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119632498
  • ISBN-13: 9781119632498
Citas grāmatas par šo tēmu:
  • Wiley Online
  • Cena: 157,82 €*
  • * this price gives unlimited concurrent access for unlimited time
Network Traffic Engineering - Stochastic Models and Applications: Stochastic Models and ApplicationsPalielināt
  • Formāts: 816 pages
  • Izdošanas datums: 02-Oct-2020
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119632498
  • ISBN-13: 9781119632498
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119632498
"This book provides an advanced level queuing theory guide for students with a strong mathematical background who are interested in analytic modeling of communication networks. It begins with the basics of queueing theory before moving on to more advanced levels. The topics covered in the book and their organization and presentation come out of research work, project development, teaching activity and discussions drawn from the author's professional experience. Topics are selected for their relevance, soas to provide a consistent view of most useful models. Examples are taken from real technical problems or engineering applications of interest to current and foreseeable future systems (e.g., LTE, Wi-Fi, ad-hoc networks, automated vehicles, reliability).They show how insight into real-world problems can be gained by means of quantitative modeling"--

A COMPREHENSIVE GUIDE TO THE CONCEPTS AND APPLICATIONS OF QUEUING THEORY AND TRAFFIC THEORY

Network Traffic Engineering: Stochastic Models and Applications provides an advanced level queuing theory guide for students with a strong mathematical background who are interested in analytic modeling and performance assessment of service system networks, with a focus on communication networks.

The text begins with the basics of queuing theory before moving on to more advanced levels. Examples and applications are a key part of the material. The topics covered in the book are derived from cutting-edge research, project development, teaching activity, and discussions on the subject. They include applications of queuing and traffic theory in:

  • Cellular networks
  • Wi-Fi networks
  • Ad-hoc and vehicular networks
  • Congestion control in the Internet

The distinguished author seeks to show how insight into practical and real-world problems can be gained by means of quantitative modeling. Perfect for graduate and PhD students of engineering and science in the field of Information and Communication Technologies, including computer, telecommunications, and electrical engineering, computer science, data science, Network Traffic Engineering offers a supremely practical approach, grounded on a solid theoretical foundation, to a rapidly developing field of study and industry.

Preface xvii
Acronyms xix
Part I Models for Service Systems
1(130)
1 Introduction
3(30)
1.1 Network Traffic Engineering: What, Why, How
3(5)
1.2 The Art of Modeling
8(5)
1.3 An Example: Delay Equalization
13(8)
1.3.1 Model Setting
14(1)
1.3.2 Analysis by Equations
15(4)
1.3.3 Analysis by Simulation
19(2)
1.3.4 Takeaways
21(1)
1.4 Outline of the Book
21(8)
1.4.1 Plan
21(4)
1.4.2 Use
25(2)
1.4.3 Notation
27(2)
1.5 Further Readings
29(4)
Problems
30(3)
2 Service Systems and Queues
33(38)
2.1 Service System Structure
33(2)
2.2 Arrival and Service Processes
35(3)
2.3 The Queue as a Service System Model
38(2)
2.4 Queues in Equilibrium
40(9)
2.4.1 Queues and Stationary Processes
40(5)
2.4.2 Little's Law
45(4)
2.5 Palm's Distributions for a Queue
49(4)
2.6 The Traffic Process
53(3)
2.7 Performance Metrics
56(5)
2.7.1 Throughput
56(3)
2.7.2 Utilization
59(1)
2.7.3 Loss
59(2)
2.1 A Delay
61(10)
2.7.5 Age of Information
62(1)
Summary and Takeaways
63(2)
Problems
65(6)
3 Stochastic Models for Network Traffic
71(60)
3.1 Introduction
71(1)
3.2 The Poisson Process
72(28)
3.2.1 Light versus Heavy Tails
78(1)
3.2.2 Inhomogeneous Poisson Process
79(5)
3.2.3 Poisson Process in Multidimensional Spaces
84(5)
3.2.3.1 Displacement
89(1)
3.2.3.2 Mapping
89(1)
3.2.3.3 Thinning
90(1)
3.2.3.4 Distances
91(1)
3.2.3.5 Sums and Products on Point Processes
92(2)
3.2.3.6 Hard Core Processes
94(2)
3.2.4 Testing for Poisson
96(4)
3.3 The Markovian Arrival Process
100(3)
3.4 Renewal Processes
103(12)
3.4.1 Residual Inter-Event Time and Renewal Paradox
108(2)
3.4.2 Superposition of Renewal Processes
110(1)
3.4.3 Alternating Renewal Processes
111(2)
3.4.4 Renewal Reward Processes
113(2)
3.5 Birth-Death Processes
115(6)
3.6 Branching Processes
121(10)
Summary and Takeaways
125(1)
Problems
126(5)
Part II Queues
131(346)
4 Single-Server Queues
133(66)
4.1 Introduction and Notation
133(1)
4.2 The Embedded Markov Chain Analysis of the M/G/1 Queue
134(18)
4.2.1 Queue Length
136(5)
4.2.2 Waiting Time
141(4)
4.2.3 Busy Period and Idle Time
145(3)
4.2.4 Remaining Service Time
148(1)
4.2.5 Output Process
149(2)
4.2.6 Evaluation of the Probabilities {ak}kεz
151(1)
4.3 The M/G/1/K Queue
152(14)
4.3.1 Exact Solution
153(4)
4.3.2 Asymptotic Approximation for Large K
157(9)
4.4 Numerical Evaluation of the Queue Length PDF
166(2)
4.5 A Special Case: the M/M/1 Queue
168(2)
4.6 Optimization of a Single-Server Queue
170(8)
4.6.1 Maximization of Net Profit
171(3)
4.6.2 Minimization of Age of Information
174(1)
4.6.2.1 General Expression of the Average Age of Information
175(2)
4.6.2.2 Minimization of the Age of Information for an M/M/1 Model
177(1)
4.7 The G/M/1 Queue
178(7)
4.8 Matrix-Geometric Queues
185(7)
4.8.1 Quasi Birth-Death (QBD) Processes
186(2)
4.8.2 M/G/1 and G/M/1 Structured Processes
188(4)
4.9 A General Result on Single-Server Queues
192(7)
Summary and Takeaways
194(1)
Problems
195(4)
5 Multi-Server Queues
199(66)
5.1 Introduction
199(2)
5.2 The Erlang Loss System
201(23)
5.2.1 Insensitivity Property of the Erlang Loss System
211(2)
5.2.2 A Finite Population Model
213(1)
5.2.3 Non-Poisson Input Traffic
214(3)
5.2.3.1 Wilkinson's Method
217(1)
5.2.3.2 Fredericks' Method
218(3)
5.2.4 Multi-Class Erlang Loss System
221(3)
5.3 Application of the Erlang Loss Model to Cellular Radio Access Network
224(14)
5.3.1 Cell Dimensioning under Quality of Service Constraints
225(5)
5.3.2 Number of Handoffs in a Connection Lifetime
230(2)
5.3.3 Blocking in a Cell with User Mobility
232(2)
5.3.4 Trade-off between Location Updating and Paging
234(2)
5.3.5 Dimensioning of a Cell with Two Service Classes
236(2)
5.4 The M/M/m Queue
238(9)
5.4.1 Finite Queue Size Model
243(1)
5.4.2 Resource Sharing versus Isolation
244(3)
5.5 Infinite Server Queues
247(18)
5.5.1 Analysis of Message Propagation in a Linear Network
252(5)
Summary and Takeaways
257(1)
Problems
258(7)
6 Priorities and Scheduling
265(66)
6.1 Introduction
265(3)
6.2 Conservation Law
268(4)
6.3 M/G/1 Priority Queueing
272(17)
6.3.1 Non-FCFS Queueing Disciplines
273(3)
6.3.2 Head-of-Line (HOL) Priorities
276(7)
6.3.3 Preempt-Resume Priorities
283(1)
6.3.4 Shortest Job First
284(2)
6.3.5 Shortest Remaining Processing Time
286(2)
6.3.6 The μC Rule
288(1)
6.4 Processor Sharing
289(23)
6.4.1 The M/G/1 Processor Sharing Model
290(3)
6.4.2 Generalized Processor Sharing
293(5)
6.4.3 Weighted Fair Queueing
298(4)
6.4.4 Credit-Based Scheduling
302(4)
6.4.5 Deficit Round Robin Scheduling
306(2)
6.4.6 Least Attained Service Scheduling
308(4)
6.5 Miscellaneous Scheduling
312(12)
6.5.1 Scheduling on a Radio Link
312(1)
6.5.1.1 Proportional Fairness
312(1)
6.5.1.2 Multi-rate Orthogonal Multiplexing
313(5)
6.5.2 Job Dispatching
318(6)
6.6 Optimal Scheduling
324(7)
6.6.1 Anticipative Systems
325(1)
6.6.2 Server-Sharing, Nonanticipative Systems
325(1)
6.6.3 Non-Server-Sharing, Nonanticipative Systems
326(1)
Summary and Takeaways
327(1)
Problems
327(4)
7 Queueing Networks
331(68)
7.1 Structure of a Queueing Network and Notation
331(1)
7.2 Open Queueing Networks
332(23)
7.2.1 Optimization of Network Capacities
345(2)
7.2.2 Optimal Routing
347(3)
7.2.3 Braess Paradox
350(5)
7.3 Closed Queueing Networks
355(14)
7.3.1 Arrivals See Time Averages (ASTA)
358(1)
7.3.2 Buzen's Algorithm for the Computation of the Normalization Constant
359(1)
7.3.3 Mean Value Analysis
360(9)
7.4 Loss Networks
369(12)
7.4.1 Erlang Fixed-Point Approximation
373(5)
7.4.2 Alternate Routing
378(3)
7.5 Stability of Queueing Networks
381(9)
7.5.1 Definition of Stability
385(2)
7.5.2 Turning a Stochastic Discrete Queueing Network into a Deterministic Fluid Network
387(3)
7.6 Further Readings
390(9)
Appendix
391(3)
Summary and Takeaways
394(1)
Problems
394(5)
8 Bounds and Approximations
399(78)
8.1 Introduction
399(2)
8.2 Bounds for the G/G/1 Queue
401(11)
8.2.1 Mean Value Analysis
404(2)
8.2.2 Output Process
406(1)
8.2.3 Upper and Lower Bounds of the Mean Waiting Time
407(2)
8.2.4 Upper Bound of the Waiting Time Probability Distribution
409(3)
8.3 Bounds for the G/G/m Queue
412(4)
8.4 Approximate Analysis of Isolated G/G Queues
416(10)
8.4.1 Approximations from Bounds
416(1)
8.4.2 Approximation of the Arrival or Service Process
417(1)
8.4.3 Reflected Brownian Motion Approximation
418(5)
8.4.4 Heavy-traffic Approximation
423(3)
8.5 Approximate Analysis of a Network of G/G/1 Queues
426(17)
8.5.1 Superposition of Flows
427(1)
8.5.2 Flow Through a Queue
428(1)
8.5.3 Bernoulli Splitting of a Flow
428(1)
8.5.4 Putting Pieces Together: The Decomposition Method
429(13)
8.5.5 Bottleneck Approximation for Closed Queueing Networks
442(1)
8.6 Fluid Models
443(34)
8.6.1 Deterministic Fluid Model
444(8)
8.6.2 From Fluid to Diffusion Model
452(4)
8.6.3 Stochastic Fluid Model
456(3)
8.6.4 Steady-State Analysis
459(3)
8.6.4.1 Infinite Buffer Size (K = ∞)
462(1)
8.6.4.2 Loss Probability
463(3)
8.6.5 First Passage Times
466(2)
8.6.6 Application of the Stochastic Fluid Model to a Multiplexer with ON-OFF Traffic Sources
468(3)
Summary and Takeaways
471(1)
Problems
472(5)
Part III Networked Systems and Protocols
477(258)
9 Multiple Access
479(34)
9.1 Introduction
479(3)
9.2 Slotted ALOHA
482(17)
9.2.1 Analysis of the Naive Slotted ALOHA
483(4)
9.2.2 Finite Population Slotted ALOHA
487(7)
9.2.3 Stabilized Slotted ALOHA
494(5)
9.3 Pure ALOHA with Variable Packet Times
499(5)
9.4 Carrier Sense Multiple Access (CSMA)
504(9)
9.4.1 Features of the CSMA Protocol
505(1)
9.4.1.1 Clear Channel Assessment
505(1)
9.4.1.2 Persistence Policy
506(1)
9.4.1.3 Retransmission Policy
507(2)
9.4.2 Finite Population Model of CSMA
509(4)
9 A3 Multi-Packet Reception CSMA
513(66)
9.4.3.1 Multi-Packet Reception 1-Persistent CSMA with Poisson Traffic
515(4)
9.4.3.2 Multi-Packet Reception Nonpersistent CSMA with Poisson Traffic
519(4)
9.4.4 Stability of CSMA
523(8)
9.4.5 Delay Analysis of Stabilized CSMA
531(3)
9.5 Analysis of the WiFi MAC Protocol
534(36)
9.5.1 Outline of the IEEE 802.11 DCF Protocol
534(4)
9.5.2 Model of CSMA/CA
538(2)
9.5.2.1 The Back-off Process
540(3)
9.5.2.2 Virtual Slot Time
543(2)
9.5.2.3 Saturation Throughput
545(4)
9.5.2.4 Service Times of IEEE 802.11 DCF
549(5)
9.5.2.5 Correlation between Service Times
554(2)
9.5.3 Optimization of Back-off Parameters
556(1)
9.5.3.1 Maximization of Throughput
556(5)
9.5.3.2 Minimization of Service Time Jitter
561(4)
9.5.4 Fairness of CSMA/CA
565(5)
9.6 Further Readings
570(9)
Appendix
572(1)
Summary and Takeaways
573(2)
Problems
575(4)
10 Congestion Control
579(90)
10.1 Introduction
579(4)
10.2 Congestion Control Architecture in the Internet
583(4)
10.3 Evolution of Congestion Control in the Internet
587(24)
10.3.1 TCP Reno
588(1)
10.3.1.1 TCP Congestion Control Operations
589(4)
10.3.1.2 NewReno
593(1)
10.3.1.3 TCP Congestion Control with SACK
594(1)
10.3.1.4 Congestion Window Validation
595(1)
10.3.2 TCP CUBIC
596(2)
10.3.3 TCP Vegas
598(3)
10.3.4 Data Center TCP (DCTCP)
601(1)
10.3.4.1 Marking at the Switch
602(1)
10.3.4.2 ECN-Echo at the Receiver
603(1)
10.3.4.3 Controller at the Sender
603(1)
10.3.5 Bottleneck Bandwidth and RTT (BBR)
604(3)
10.3.5.1 Delivery Rate Estimate
607(1)
10.3.5.2 Startup and Drain
608(1)
10.3.5.3 Probe BW
609(1)
10.3.5.4 Probe RTT
610(1)
10.3.5.5 Pseudo-code of BBR Algorithm
610(1)
10.4 Traffic Engineering with TCP
611(3)
10.5 Fluid Model of a Single TCP Connection Congestion Control
614(21)
10.5.1 Classic TCP with Fixed Capacity Bottleneck Link
615(2)
10.5.2 Classic TCP with Variable Capacity Bottleneck Link
617(8)
10.5.2.1 Discretization of the Evolution Equations
625(2)
10.5.2.2 Accuracy of the Fluid Approximation of TCP
627(3)
10.5.3 Application to Wireless Links
630(1)
10.5.3.1 Random Capacity
630(2)
10.5.3.2 TCP over Cellular Link
632(3)
10.6 Fluid Model of Multiple TCP Connections Congestion Control
635(7)
10.6.1 Negligible Buffering at the Bottleneck
635(2)
10.6.2 Classic TCP with Drop Tail Buffer at the Bottleneck
637(1)
10.6.3 Classic TCP with AQM at the Bottleneck
638(1)
10.6.4 Data Center TCP with FIFO Buffer at the Bottleneck
639(3)
10.7 Fairness and Congestion Control
642(3)
10.8 Network Utility Maximization (NUM)
645(7)
10.9 Challenges to TCP
652(17)
10.9.1 Fat-Long Pipes
653(2)
10.9.2 Wireless Channels
655(1)
10.9.3 Bufferbloat
656(2)
10.9.4 Interaction with Applications
658(1)
Appendix
659(5)
Summary and Takeaways
664(1)
Problems
665(4)
11 Quality-of-Service Guarantees
669(66)
11.1 Introduction
669(1)
11.2 Deterministic Service Guarantees
670(33)
11.2.1 Arrival Curves
673(4)
11.2.2 Service Curves
677(4)
11.2.3 Performance Bounds
681(2)
11.2.4 Regulators
683(5)
11.2.5 Network Calculus
688(1)
11.2.5.1 Single Node Analysis
689(3)
11.2.5.2 End-to-End Analysis
692(11)
11.3 Stochastic Service Guarantees
703(24)
11.3.1 Multiplexing with Marginal Buffer Size
703(8)
11.3.2 Multiplexing with Non-Negligible Buffer Size
711(3)
11.3.3 Effective Bandwidth
714(1)
11.3.3.1 Definition of the Effective Bandwidth
714(1)
11.3.3.2 Properties of the Effective Bandwidth
715(1)
11.3.3.3 Effective Bandwidth of a Markov Source
716(5)
11.3.4 Network Analysis and Dimensioning
721(6)
11.4 Further Readings
727(8)
Appendix
728(4)
Summary and Takeaways
732(1)
Problems
733(2)
A Refresher of Probability, Random Variables, and Stochastic Processes
735(40)
A.1 Probability
735(2)
A.2 Random Variables
737(2)
A.3 Transforms of Probability Distribution Functions
739(5)
A.4 Inequalities and Limit Theorems
744(4)
A.4.1 Markov Inequality
744(1)
A.4.2 Chebychev Inequality
745(1)
A.4.3 Jensen Inequality
746(1)
A.4.4 Chernov Bound
746(1)
A.4.5 Union Bound
747(1)
A.4.6 Central Limit Theorem (CLT)
747(1)
A.5 Stochastic Processes
748(1)
A.6 Markov Chains
749(20)
A.6.1 Classification of States
750(1)
A.6.2 Recurrence
751(3)
A.6.3 Visits to a State
754(2)
A.6.4 Asymptotic Behavior and Steady State
756(6)
A.6.5 Absorbing Markov Chains
762(1)
A.6.6 Continuous-Time Markov Processes
763(2)
A.6.7 Sojourn Times in Process States
765(1)
A.6.8 Reversibility
766(2)
A.6.9 Uniformization
768(1)
A.7 Wiener Process (Brownian Motion)
769(6)
A.7.1 Wiener Process with an Absorbing Barrier
771(1)
A.7.2 Wiener Process with a Reflecting Barrier
772(3)
References 775(14)
Index 789
ANDREA BAIOCCHI, PhD, is a Full Professor in the Department of Information Engineering, Electronics and Telecommunications of the University of Roma "La Sapienza". He has published over 160 papers on international journals and conference proceedings. He has participated to the Technical Program Committees of more than seventy international conferences. He served in the editorial board of the telecommunications technical journal published by Telecom Italia (currently TIM) for ten years.
Permanent link: https://www.kriso.lv/db/9781119632498_pe.html
Keywords: