{"product_id":"network-traffic-engineering-isbn-9781119632436","title":"Network Traffic Engineering","description":"\u003cp\u003e\u003cb\u003eA comprehensive guide to the concepts and applications of queuing theory and traffic theory\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eNetwork Traffic Engineering: Models and Applications\u003c\/i\u003e provides an advanced level queuing theory guide for students with a strong mathematical background who are interested in analytic modeling and performance assessment of communication networks.\u003c\/p\u003e \u003cp\u003eThe text begins with the basics of queueing theory before moving on to more advanced levels. The topics covered in the book are derived from the most cutting-edge research, project development, teaching activity, and discussions on the subject. They include applications of queuing and traffic theory in:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLTE networks\u003c\/li\u003e \u003cli\u003eWi-Fi networks\u003c\/li\u003e \u003cli\u003eAd-hoc networks\u003c\/li\u003e \u003cli\u003eAutomated vehicles\u003c\/li\u003e \u003cli\u003eCongestion control on the Internet\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe distinguished author seeks to show how insight into practical and real-world problems can be gained by means of quantitative modeling. Perfect for graduate students of computer engineering, computer science, telecommunication engineering, and electrical engineering, \u003ci\u003eNetwork Traffic Engineering\u003c\/i\u003e offers a supremely practical approach to a rapidly developing field of study and industry.\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcronyms xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Models for Service Systems \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003cb\u003e3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Network Traffic Engineering: What, Why, How 3\u003c\/p\u003e \u003cp\u003e1.2 The Art of Modeling 8\u003c\/p\u003e \u003cp\u003e1.3 An Example: Delay Equalization 13\u003c\/p\u003e \u003cp\u003e1.3.1 Model Setting 14\u003c\/p\u003e \u003cp\u003e1.3.2 Analysis by Equations 15\u003c\/p\u003e \u003cp\u003e1.3.3 Analysis by Simulation 19\u003c\/p\u003e \u003cp\u003e1.3.4 Takeaways 21\u003c\/p\u003e \u003cp\u003e1.4 Outline of the Book 21\u003c\/p\u003e \u003cp\u003e1.4.1 Plan 21\u003c\/p\u003e \u003cp\u003e1.4.2 Use 25\u003c\/p\u003e \u003cp\u003e1.4.3 Notation 27\u003c\/p\u003e \u003cp\u003e1.5 Further Readings 29\u003c\/p\u003e \u003cp\u003eProblems 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Service Systems and Queues \u003c\/b\u003e\u003cb\u003e33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Service System Structure 33\u003c\/p\u003e \u003cp\u003e2.2 Arrival and Service Processes 35\u003c\/p\u003e \u003cp\u003e2.3 The Queue as a Service System Model 38\u003c\/p\u003e \u003cp\u003e2.4 Queues in Equilibrium 40\u003c\/p\u003e \u003cp\u003e2.4.1 Queues and Stationary Processes 40\u003c\/p\u003e \u003cp\u003e2.4.2 Little’s Law 45\u003c\/p\u003e \u003cp\u003e2.5 Palm’s Distributions for a Queue 49\u003c\/p\u003e \u003cp\u003e2.6 The Traffic Process 53\u003c\/p\u003e \u003cp\u003e2.7 Performance Metrics 56\u003c\/p\u003e \u003cp\u003e2.7.1 Throughput 56\u003c\/p\u003e \u003cp\u003e2.7.2 Utilization 59\u003c\/p\u003e \u003cp\u003e2.7.3 Loss 59\u003c\/p\u003e \u003cp\u003e2.7.4 Delay 61\u003c\/p\u003e \u003cp\u003e2.7.5 Age of Information 62\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 63\u003c\/p\u003e \u003cp\u003eProblems 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Stochastic Models for Network Traffic \u003c\/b\u003e\u003cb\u003e71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 71\u003c\/p\u003e \u003cp\u003e3.2 The Poisson Process 72\u003c\/p\u003e \u003cp\u003e3.2.1 Light versus Heavy Tails 78\u003c\/p\u003e \u003cp\u003e3.2.2 Inhomogeneous Poisson Process 79\u003c\/p\u003e \u003cp\u003e3.2.3 Poisson Process in Multidimensional Spaces 84\u003c\/p\u003e \u003cp\u003e3.2.3.1 Displacement 89\u003c\/p\u003e \u003cp\u003e3.2.3.2 Mapping 89\u003c\/p\u003e \u003cp\u003e3.2.3.3 Thinning 90\u003c\/p\u003e \u003cp\u003e3.2.3.4 Distances 91\u003c\/p\u003e \u003cp\u003e3.2.3.5 Sums and Products on Point Processes 92\u003c\/p\u003e \u003cp\u003e3.2.3.6 Hard Core Processes 94\u003c\/p\u003e \u003cp\u003e3.2.4 Testing for Poisson 96\u003c\/p\u003e \u003cp\u003e3.3 The Markovian Arrival Process 100\u003c\/p\u003e \u003cp\u003e3.4 Renewal Processes 103\u003c\/p\u003e \u003cp\u003e3.4.1 Residual Inter-Event Time and Renewal Paradox 108\u003c\/p\u003e \u003cp\u003e3.4.2 Superposition of Renewal Processes 110\u003c\/p\u003e \u003cp\u003e3.4.3 Alternating Renewal Processes 111\u003c\/p\u003e \u003cp\u003e3.4.4 Renewal Reward Processes 113\u003c\/p\u003e \u003cp\u003e3.5 Birth-Death Processes 115\u003c\/p\u003e \u003cp\u003e3.6 Branching Processes 121\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 125\u003c\/p\u003e \u003cp\u003eProblems 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Queues \u003c\/b\u003e\u003cb\u003e131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Single-Server Queues \u003c\/b\u003e\u003cb\u003e133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction and Notation 133\u003c\/p\u003e \u003cp\u003e4.2 The Embedded Markov Chain Analysis of the \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 Queue 134\u003c\/p\u003e \u003cp\u003e4.2.1 Queue Length 136\u003c\/p\u003e \u003cp\u003e4.2.2 Waiting Time 141\u003c\/p\u003e \u003cp\u003e4.2.3 Busy Period and Idle Time 145\u003c\/p\u003e \u003cp\u003e4.2.4 Remaining Service Time 148\u003c\/p\u003e \u003cp\u003e4.2.5 Output Process 149\u003c\/p\u003e \u003cp\u003e4.2.6 Evaluation of the Probabilities {\u003ci\u003ea\u003csub\u003ek\u003c\/sub\u003e\u003c\/i\u003e}\u003ci\u003e\u003csub\u003ek\u003c\/sub\u003e\u003c\/i\u003e\u003csub\u003e∈ℤ\u003c\/sub\u003e 151\u003c\/p\u003e \u003cp\u003e4.3 The \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1∕\u003ci\u003eK \u003c\/i\u003eQueue 152\u003c\/p\u003e \u003cp\u003e4.3.1 Exact Solution 153\u003c\/p\u003e \u003cp\u003e4.3.2 Asymptotic Approximation for Large \u003ci\u003eK \u003c\/i\u003e157\u003c\/p\u003e \u003cp\u003e4.4 Numerical Evaluation of the Queue Length PDF 166\u003c\/p\u003e \u003cp\u003e4.5 A Special Case: the \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eM\u003c\/i\u003e∕1 Queue 168\u003c\/p\u003e \u003cp\u003e4.6 Optimization of a Single-Server Queue 170\u003c\/p\u003e \u003cp\u003e4.6.1 Maximization of Net Profit 171\u003c\/p\u003e \u003cp\u003e4.6.2 Minimization of Age of Information 174\u003c\/p\u003e \u003cp\u003e4.6.2.1 General Expression of the Average Age of Information 175\u003c\/p\u003e \u003cp\u003e4.6.2.2 Minimization of the Age of Information for an \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eM\u003c\/i\u003e∕1 Model 177\u003c\/p\u003e \u003cp\u003e4.7 The \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eM\u003c\/i\u003e∕1 Queue 178\u003c\/p\u003e \u003cp\u003e4.8 Matrix-Geometric Queues 185\u003c\/p\u003e \u003cp\u003e4.8.1 Quasi Birth-Death (QBD) Processes 186\u003c\/p\u003e \u003cp\u003e4.8.2 \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 and \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eM\u003c\/i\u003e∕1 Structured Processes 188\u003c\/p\u003e \u003cp\u003e4.9 A General Result on Single-Server Queues 192\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 194\u003c\/p\u003e \u003cp\u003eProblems 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multi-Server Queues \u003c\/b\u003e\u003cb\u003e199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 199\u003c\/p\u003e \u003cp\u003e5.2 The Erlang Loss System 201\u003c\/p\u003e \u003cp\u003e5.2.1 Insensitivity Property of the Erlang Loss System 211\u003c\/p\u003e \u003cp\u003e5.2.2 A Finite Population Model 213\u003c\/p\u003e \u003cp\u003e5.2.3 Non-Poisson Input Traffic 214\u003c\/p\u003e \u003cp\u003e5.2.3.1 Wilkinson’s Method 217\u003c\/p\u003e \u003cp\u003e5.2.3.2 Fredericks’ Method 218\u003c\/p\u003e \u003cp\u003e5.2.4 Multi-Class Erlang Loss System 221\u003c\/p\u003e \u003cp\u003e5.3 Application of the Erlang Loss Model to Cellular Radio Access Network 224\u003c\/p\u003e \u003cp\u003e5.3.1 Cell Dimensioning under Quality of Service Constraints 225\u003c\/p\u003e \u003cp\u003e5.3.2 Number of Handoffs in a Connection Lifetime 230\u003c\/p\u003e \u003cp\u003e5.3.3 Blocking in a Cell with User Mobility 232\u003c\/p\u003e \u003cp\u003e5.3.4 Trade-off between Location Updating and Paging 234\u003c\/p\u003e \u003cp\u003e5.3.5 Dimensioning of a Cell with Two Service Classes 236\u003c\/p\u003e \u003cp\u003e5.4 The \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003em \u003c\/i\u003eQueue 238\u003c\/p\u003e \u003cp\u003e5.4.1 Finite Queue Size Model 243\u003c\/p\u003e \u003cp\u003e5.4.2 Resource Sharing versus Isolation 244\u003c\/p\u003e \u003cp\u003e5.5 Infinite Server Queues 247\u003c\/p\u003e \u003cp\u003e5.5.1 Analysis of Message Propagation in a Linear Network 252\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 257\u003c\/p\u003e \u003cp\u003eProblems 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Priorities and Scheduling \u003c\/b\u003e\u003cb\u003e265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 265\u003c\/p\u003e \u003cp\u003e6.2 Conservation Law 268\u003c\/p\u003e \u003cp\u003e6.3 \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 Priority Queueing 272\u003c\/p\u003e \u003cp\u003e6.3.1 Non-FCFS Queueing Disciplines 273\u003c\/p\u003e \u003cp\u003e6.3.2 Head-of-Line (HOL) Priorities 276\u003c\/p\u003e \u003cp\u003e6.3.3 Preempt-Resume Priorities 283\u003c\/p\u003e \u003cp\u003e6.3.4 Shortest Job First 284\u003c\/p\u003e \u003cp\u003e6.3.5 Shortest Remaining Processing Time 286\u003c\/p\u003e \u003cp\u003e6.3.6 The \u003ci\u003e𝜇C \u003c\/i\u003eRule 288\u003c\/p\u003e \u003cp\u003e6.4 Processor Sharing 289\u003c\/p\u003e \u003cp\u003e6.4.1 The \u003ci\u003eM\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 Processor Sharing Model 290\u003c\/p\u003e \u003cp\u003e6.4.2 Generalized Processor Sharing 293\u003c\/p\u003e \u003cp\u003e6.4.3 Weighted Fair Queueing 298\u003c\/p\u003e \u003cp\u003e6.4.4 Credit-Based Scheduling 302\u003c\/p\u003e \u003cp\u003e6.4.5 Deficit Round Robin Scheduling 306\u003c\/p\u003e \u003cp\u003e6.4.6 Least Attained Service Scheduling 308\u003c\/p\u003e \u003cp\u003e6.5 Miscellaneous Scheduling 312\u003c\/p\u003e \u003cp\u003e6.5.1 Scheduling on a Radio Link 312\u003c\/p\u003e \u003cp\u003e6.5.1.1 Proportional Fairness 312\u003c\/p\u003e \u003cp\u003e6.5.1.2 Multi-rate Orthogonal Multiplexing 313\u003c\/p\u003e \u003cp\u003e6.5.2 Job Dispatching 318\u003c\/p\u003e \u003cp\u003e6.6 Optimal Scheduling 324\u003c\/p\u003e \u003cp\u003e6.6.1 Anticipative Systems 325\u003c\/p\u003e \u003cp\u003e6.6.2 Server-Sharing, Nonanticipative Systems 325\u003c\/p\u003e \u003cp\u003e6.6.3 Non-Server-Sharing, Nonanticipative Systems 326\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 327\u003c\/p\u003e \u003cp\u003eProblems 327\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Queueing Networks \u003c\/b\u003e\u003cb\u003e331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Structure of a Queueing Network and Notation 331\u003c\/p\u003e \u003cp\u003e7.2 Open Queueing Networks 332\u003c\/p\u003e \u003cp\u003e7.2.1 Optimization of Network Capacities 345\u003c\/p\u003e \u003cp\u003e7.2.2 Optimal Routing 347\u003c\/p\u003e \u003cp\u003e7.2.3 Braess Paradox 350\u003c\/p\u003e \u003cp\u003e7.3 Closed Queueing Networks 355\u003c\/p\u003e \u003cp\u003e7.3.1 Arrivals See Time Averages (ASTA) 358\u003c\/p\u003e \u003cp\u003e7.3.2 Buzen’s Algorithm for the Computation of the Normalization Constant 359\u003c\/p\u003e \u003cp\u003e7.3.3 Mean Value Analysis 360\u003c\/p\u003e \u003cp\u003e7.4 Loss Networks 369\u003c\/p\u003e \u003cp\u003e7.4.1 Erlang Fixed-Point Approximation 373\u003c\/p\u003e \u003cp\u003e7.4.2 Alternate Routing 378\u003c\/p\u003e \u003cp\u003e7.5 Stability of Queueing Networks 381\u003c\/p\u003e \u003cp\u003e7.5.1 Definition of Stability 385\u003c\/p\u003e \u003cp\u003e7.5.2 Turning a Stochastic Discrete Queueing Network into a Deterministic Fluid Network 387\u003c\/p\u003e \u003cp\u003e7.6 Further Readings 390\u003c\/p\u003e \u003cp\u003eAppendix 391\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 394\u003c\/p\u003e \u003cp\u003eProblems 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bounds and Approximations \u003c\/b\u003e\u003cb\u003e399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 399\u003c\/p\u003e \u003cp\u003e8.2 Bounds for the \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 Queue 401\u003c\/p\u003e \u003cp\u003e8.2.1 Mean Value Analysis 404\u003c\/p\u003e \u003cp\u003e8.2.2 Output Process 406\u003c\/p\u003e \u003cp\u003e8.2.3 Upper and Lower Bounds of the Mean Waiting Time 407\u003c\/p\u003e \u003cp\u003e8.2.4 Upper Bound of the Waiting Time Probability Distribution 409\u003c\/p\u003e \u003cp\u003e8.3 Bounds for the \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003em \u003c\/i\u003eQueue 412\u003c\/p\u003e \u003cp\u003e8.4 Approximate Analysis of Isolated \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eG \u003c\/i\u003eQueues 416\u003c\/p\u003e \u003cp\u003e8.4.1 Approximations from Bounds 416\u003c\/p\u003e \u003cp\u003e8.4.2 Approximation of the Arrival or Service Process 417\u003c\/p\u003e \u003cp\u003e8.4.3 Reflected Brownian Motion Approximation 418\u003c\/p\u003e \u003cp\u003e8.4.4 Heavy-traffic Approximation 423\u003c\/p\u003e \u003cp\u003e8.5 Approximate Analysis of a Network of \u003ci\u003eG\u003c\/i\u003e∕\u003ci\u003eG\u003c\/i\u003e∕1 Queues 426\u003c\/p\u003e \u003cp\u003e8.5.1 Superposition of Flows 427\u003c\/p\u003e \u003cp\u003e8.5.2 Flow Through a Queue 428\u003c\/p\u003e \u003cp\u003e8.5.3 Bernoulli Splitting of a Flow 428\u003c\/p\u003e \u003cp\u003e8.5.4 Putting Pieces Together: The Decomposition Method 429\u003c\/p\u003e \u003cp\u003e8.5.5 Bottleneck Approximation for Closed Queueing Networks 442\u003c\/p\u003e \u003cp\u003e8.6 Fluid Models 443\u003c\/p\u003e \u003cp\u003e8.6.1 Deterministic Fluid Model 444\u003c\/p\u003e \u003cp\u003e8.6.2 From Fluid to Diffusion Model 452\u003c\/p\u003e \u003cp\u003e8.6.3 Stochastic Fluid Model 456\u003c\/p\u003e \u003cp\u003e8.6.4 Steady-State Analysis 459\u003c\/p\u003e \u003cp\u003e8.6.4.1 Infinite Buffer Size (\u003ci\u003eK \u003c\/i\u003e= ∞) 462\u003c\/p\u003e \u003cp\u003e8.6.4.2 Loss Probability 463\u003c\/p\u003e \u003cp\u003e8.6.5 First Passage Times 466\u003c\/p\u003e \u003cp\u003e8.6.6 Application of the Stochastic Fluid Model to a Multiplexer with ON-OFF Traffic Sources 468\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 471\u003c\/p\u003e \u003cp\u003eProblems 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Networked Systems and Protocols \u003c\/b\u003e\u003cb\u003e477\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multiple Access \u003c\/b\u003e\u003cb\u003e479\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 479\u003c\/p\u003e \u003cp\u003e9.2 Slotted ALOHA 482\u003c\/p\u003e \u003cp\u003e9.2.1 Analysis of the Naïve Slotted ALOHA 483\u003c\/p\u003e \u003cp\u003e9.2.2 Finite Population Slotted ALOHA 487\u003c\/p\u003e \u003cp\u003e9.2.3 Stabilized Slotted ALOHA 494\u003c\/p\u003e \u003cp\u003e9.3 Pure ALOHA with Variable Packet Times 499\u003c\/p\u003e \u003cp\u003e9.4 Carrier Sense Multiple Access (CSMA) 504\u003c\/p\u003e \u003cp\u003e9.4.1 Features of the CSMA Protocol 505\u003c\/p\u003e \u003cp\u003e9.4.1.1 Clear Channel Assessment 505\u003c\/p\u003e \u003cp\u003e9.4.1.2 Persistence Policy 506\u003c\/p\u003e \u003cp\u003e9.4.1.3 Retransmission Policy 507\u003c\/p\u003e \u003cp\u003e9.4.2 Finite Population Model of CSMA 509\u003c\/p\u003e \u003cp\u003e9.4.3 Multi-Packet Reception CSMA 513\u003c\/p\u003e \u003cp\u003e9.4.3.1 Multi-Packet Reception 1-Persistent CSMA with Poisson Traffic 515\u003c\/p\u003e \u003cp\u003e9.4.3.2 Multi-Packet Reception Nonpersistent CSMA with Poisson Traffic 519\u003c\/p\u003e \u003cp\u003e9.4.4 Stability of CSMA 523\u003c\/p\u003e \u003cp\u003e9.4.5 Delay Analysis of Stabilized CSMA 531\u003c\/p\u003e \u003cp\u003e9.5 Analysis of the WiFi MAC Protocol 534\u003c\/p\u003e \u003cp\u003e9.5.1 Outline of the IEEE 802.11 DCF Protocol 534\u003c\/p\u003e \u003cp\u003e9.5.2 Model of CSMA\/CA 538\u003c\/p\u003e \u003cp\u003e9.5.2.1 The Back-off Process 540\u003c\/p\u003e \u003cp\u003e9.5.2.2 Virtual Slot Time 543\u003c\/p\u003e \u003cp\u003e9.5.2.3 Saturation Throughput 545\u003c\/p\u003e \u003cp\u003e9.5.2.4 Service Times of IEEE 802.11 DCF 549\u003c\/p\u003e \u003cp\u003e9.5.2.5 Correlation between Service Times 554\u003c\/p\u003e \u003cp\u003e9.5.3 Optimization of Back-off Parameters 556\u003c\/p\u003e \u003cp\u003e9.5.3.1 Maximization of Throughput 556\u003c\/p\u003e \u003cp\u003e9.5.3.2 Minimization of Service Time Jitter 561\u003c\/p\u003e \u003cp\u003e9.5.4 Fairness of CSMA\/CA 565\u003c\/p\u003e \u003cp\u003e9.6 Further Readings 570\u003c\/p\u003e \u003cp\u003eAppendix 572\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 573\u003c\/p\u003e \u003cp\u003eProblems 575\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Congestion Control \u003c\/b\u003e\u003cb\u003e579\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 579\u003c\/p\u003e \u003cp\u003e10.2 Congestion Control Architecture in the Internet 583\u003c\/p\u003e \u003cp\u003e10.3 Evolution of Congestion Control in the Internet 587\u003c\/p\u003e \u003cp\u003e10.3.1 TCP Reno 588\u003c\/p\u003e \u003cp\u003e10.3.1.1 TCP Congestion Control Operations 589\u003c\/p\u003e \u003cp\u003e10.3.1.2 NewReno 593\u003c\/p\u003e \u003cp\u003e10.3.1.3 TCP Congestion Control with SACK 594\u003c\/p\u003e \u003cp\u003e10.3.1.4 Congestion Window Validation 595\u003c\/p\u003e \u003cp\u003e10.3.2 TCP CUBIC 596\u003c\/p\u003e \u003cp\u003e10.3.3 TCP Vegas 598\u003c\/p\u003e \u003cp\u003e10.3.4 Data Center TCP (DCTCP) 601\u003c\/p\u003e \u003cp\u003e10.3.4.1 Marking at the Switch 602\u003c\/p\u003e \u003cp\u003e10.3.4.2 ECN-Echo at the Receiver 603\u003c\/p\u003e \u003cp\u003e10.3.4.3 Controller at the Sender 603\u003c\/p\u003e \u003cp\u003e10.3.5 Bottleneck Bandwidth and RTT (BBR) 604\u003c\/p\u003e \u003cp\u003e10.3.5.1 Delivery Rate Estimate 607\u003c\/p\u003e \u003cp\u003e10.3.5.2 StartUp and Drain 608\u003c\/p\u003e \u003cp\u003e10.3.5.3 ProbeBW 609\u003c\/p\u003e \u003cp\u003e10.3.5.4 ProbeRTT 610\u003c\/p\u003e \u003cp\u003e10.3.5.5 Pseudo-code of BBR Algorithm 610\u003c\/p\u003e \u003cp\u003e10.4 Traffic Engineering with TCP 611\u003c\/p\u003e \u003cp\u003e10.5 Fluid Model of a Single TCP Connection Congestion Control 614\u003c\/p\u003e \u003cp\u003e10.5.1 Classic TCP with Fixed Capacity Bottleneck Link 615\u003c\/p\u003e \u003cp\u003e10.5.2 Classic TCP with Variable Capacity Bottleneck Link 617\u003c\/p\u003e \u003cp\u003e10.5.2.1 Discretization of the Evolution Equations 625\u003c\/p\u003e \u003cp\u003e10.5.2.2 Accuracy of the Fluid Approximation of TCP 627\u003c\/p\u003e \u003cp\u003e10.5.3 Application to Wireless Links 630\u003c\/p\u003e \u003cp\u003e10.5.3.1 Random Capacity 630\u003c\/p\u003e \u003cp\u003e10.5.3.2 TCP over Cellular Link 632\u003c\/p\u003e \u003cp\u003e10.6 Fluid Model of Multiple TCP Connections Congestion Control 635\u003c\/p\u003e \u003cp\u003e10.6.1 Negligible Buffering at the Bottleneck 635\u003c\/p\u003e \u003cp\u003e10.6.2 Classic TCP with Drop Tail Buffer at the Bottleneck 637\u003c\/p\u003e \u003cp\u003e10.6.3 Classic TCP with AQM at the Bottleneck 638\u003c\/p\u003e \u003cp\u003e10.6.4 Data Center TCP with FIFO Buffer at the Bottleneck 639\u003c\/p\u003e \u003cp\u003e10.7 Fairness and Congestion Control 642\u003c\/p\u003e \u003cp\u003e10.8 Network Utility Maximization (NUM) 645\u003c\/p\u003e \u003cp\u003e10.9 Challenges to TCP 652\u003c\/p\u003e \u003cp\u003e10.9.1 Fat-Long Pipes 653\u003c\/p\u003e \u003cp\u003e10.9.2 Wireless Channels 655\u003c\/p\u003e \u003cp\u003e10.9.3 Bufferbloat 656\u003c\/p\u003e \u003cp\u003e10.9.4 Interaction with Applications 658\u003c\/p\u003e \u003cp\u003eAppendix 659\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 664\u003c\/p\u003e \u003cp\u003eProblems 665\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Quality-of-Service Guarantees \u003c\/b\u003e\u003cb\u003e669\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 669\u003c\/p\u003e \u003cp\u003e11.2 Deterministic Service Guarantees 670\u003c\/p\u003e \u003cp\u003e11.2.1 Arrival Curves 673\u003c\/p\u003e \u003cp\u003e11.2.2 Service Curves 677\u003c\/p\u003e \u003cp\u003e11.2.3 Performance Bounds 681\u003c\/p\u003e \u003cp\u003e11.2.4 Regulators 683\u003c\/p\u003e \u003cp\u003e11.2.5 Network Calculus 688\u003c\/p\u003e \u003cp\u003e11.2.5.1 Single Node Analysis 689\u003c\/p\u003e \u003cp\u003e11.2.5.2 End-to-End Analysis 692\u003c\/p\u003e \u003cp\u003e11.3 Stochastic Service Guarantees 703\u003c\/p\u003e \u003cp\u003e11.3.1 Multiplexing with Marginal Buffer Size 703\u003c\/p\u003e \u003cp\u003e11.3.2 Multiplexing with Non-Negligible Buffer Size 711\u003c\/p\u003e \u003cp\u003e11.3.3 Effective Bandwidth 714\u003c\/p\u003e \u003cp\u003e11.3.3.1 Definition of the Effective Bandwidth 714\u003c\/p\u003e \u003cp\u003e11.3.3.2 Properties of the Effective Bandwidth 715\u003c\/p\u003e \u003cp\u003e11.3.3.3 Effective Bandwidth of a Markov Source 716\u003c\/p\u003e \u003cp\u003e11.3.4 Network Analysis and Dimensioning 721\u003c\/p\u003e \u003cp\u003e11.4 Further Readings 727\u003c\/p\u003e \u003cp\u003eAppendix 728\u003c\/p\u003e \u003cp\u003eSummary and Takeaways 732\u003c\/p\u003e \u003cp\u003eProblems 733\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Refresher of Probability, Random Variables, and Stochastic Processes \u003c\/b\u003e\u003cb\u003e735\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Probability 735\u003c\/p\u003e \u003cp\u003eA.2 Random Variables 737\u003c\/p\u003e \u003cp\u003eA.3 Transforms of Probability Distribution Functions 739\u003c\/p\u003e \u003cp\u003eA.4 Inequalities and Limit Theorems 744\u003c\/p\u003e \u003cp\u003eA.4.1 Markov Inequality 744\u003c\/p\u003e \u003cp\u003eA.4.2 Chebychev Inequality 745\u003c\/p\u003e \u003cp\u003eA.4.3 Jensen Inequality 746\u003c\/p\u003e \u003cp\u003eA.4.4 Chernov Bound 746\u003c\/p\u003e \u003cp\u003eA.4.5 Union Bound 747\u003c\/p\u003e \u003cp\u003eA.4.6 Central Limit Theorem (CLT) 747\u003c\/p\u003e \u003cp\u003eA.5 Stochastic Processes 748\u003c\/p\u003e \u003cp\u003eA.6 Markov Chains 749\u003c\/p\u003e \u003cp\u003eA.6.1 Classification of States 750\u003c\/p\u003e \u003cp\u003eA.6.2 Recurrence 751\u003c\/p\u003e \u003cp\u003eA.6.3 Visits to a State 754\u003c\/p\u003e \u003cp\u003eA.6.4 Asymptotic Behavior and Steady State 756\u003c\/p\u003e \u003cp\u003eA.6.5 Absorbing Markov Chains 762\u003c\/p\u003e \u003cp\u003eA.6.6 Continuous-Time Markov Processes 763\u003c\/p\u003e \u003cp\u003eA.6.7 Sojourn Times in Process States 765\u003c\/p\u003e \u003cp\u003eA.6.8 Reversibility 766\u003c\/p\u003e \u003cp\u003eA.6.9 Uniformization 768\u003c\/p\u003e \u003cp\u003eA.7 Wiener Process (Brownian Motion) 769\u003c\/p\u003e \u003cp\u003eA.7.1 Wiener Process with an Absorbing Barrier 771\u003c\/p\u003e \u003cp\u003eA.7.2 Wiener Process with a Reflecting Barrier 772\u003c\/p\u003e \u003cp\u003eReferences 775\u003c\/p\u003e \u003cp\u003eIndex 789\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eANDREA BAIOCCHI,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA COMPREHENSIVE GUIDE TO THE CONCEPTS AND APPLICATIONS OF QUEUING THEORY AND TRAFFIC THEORY\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eNetwork Traffic Engineering: Stochastic Models and Applications\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThe 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCellular networks\u003c\/li\u003e \u003cli\u003eWi-Fi networks\u003c\/li\u003e \u003cli\u003eAd-hoc and vehicular networks\u003c\/li\u003e \u003cli\u003eCongestion control in the Internet\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989681783013,"sku":"NP9781119632436","price":123.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119632436.jpg?v=1761785083","url":"https:\/\/k12savings.com\/products\/network-traffic-engineering-isbn-9781119632436","provider":"K12savings","version":"1.0","type":"link"}