Atjaunināt sīkdatņu piekrišanu

E-grāmata: Resource Allocation and Performance Optimization in Communication Networks and the Internet

4.50/5 (2 ratings by Goodreads)
(Central China Normal University, Wuhan, Hubei, People's Republic of China)
  • Formāts: 523 pages
  • Izdošanas datums: 15-Aug-2017
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781498769457
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 58,85 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Resource Allocation and Performance Optimization in Communication Networks and the InternetPalielināt
  • Formāts: 523 pages
  • Izdošanas datums: 15-Aug-2017
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781498769457
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book provides a comprehensive introduction to the underlying theory, design techniques and analytical results of wireless communication networks, focusing on the core principles of wireless network design. It elaborates the network utility maximization (NUM) theory with applications in resource allocation of wireless networks, with a central aim of design and the QoS guarantee. It presents and discusses state-of-the-art developments in resource allocation and performance optimization in wireless communication networks. It provides an overview of the general background including the basic wireless communication networks and the relevant protocols, architectures, methods and algorithms.
Preface xv
Acknowledgments xix
Chapter 1 Introduction to Wireless Networks: Evolving Communication Technology 1(20)
1.1 Evolution of Wireless Networks
1(1)
1.2 1G Wireless Networks
2(2)
1.3 2G Wireless Networks
4(3)
1.4 3G Wireless Networks
7(2)
1.5 4G Wireless Networks
9(2)
1.6 5G Wireless Networks
11(7)
1.7 Future Trends on Wireless Communication Networks
18(3)
1.7.1 Supporting Cloud Computing
18(1)
1.7.2 Supporting IoT
19(2)
Chapter 2 Network Utility Maximization (NUM) Theory 21(14)
2.1 Utility and Utility Function
21(7)
2.2 NUM Theory
28(2)
2.3 Application of NUM: Reverse Engineering of TCP Reno
30(5)
Chapter 3 Congestion Control Approaches: Primal Algorithm, Dual Algorithm, and Primal-Dual Algorithm 35(42)
3.1 Basic Model for Congestion Control in the Internet
37(1)
3.2 Solving the Saddle Equilibrium Point Equations to Obtain the Individual Flow Rate Vector and the Link Price Vector
38(4)
3.3 Example of Solving the Saddle Equilibrium Point Equations
42(3)
3.4 Primal Algorithm
45(2)
3.5 Dual Algorithm
47(2)
3.6 Primal-Dual Algorithm
49(1)
3.7 Stability of Primal-Dual Algorithms toward Satisfactory Performance
50(22)
3.7.1 Analyses to the Primal-Dual Algorithm
51(4)
3.7.2 Stability Results for a Dumbbell Network
55(4)
3.7.3 Applications to Protocol Design and Parameter Setting of FAST TCP
59(1)
3.7.4 Simulation Results
60(5)
3.7.5 More Results on Parameter Tuning of FAST TCP
65(1)
3.7.6 Analyses of the FAST TCP Model and Results on Stability
66(4)
3.7.7 Simulation Verification
70(1)
3.7.8 Concluding Remarks
71(1)
3.8 Stability Analyses of a Primal-Dual Algorithm: FAST TCP over RED
72(5)
3.8.1 Primal-Dual Model: Definitions and Notations
72(1)
3.8.2 Modified Model of FAST TCP
72(1)
3.8.3 Closed-Loop Feedback System of FAST TCP/RED
73(1)
3.8.4 Stability Analysis
73(3)
3.8.5 Simulation Results
76(1)
3.8.6 Conclusions
76(1)
Chapter 4 FAST TCP and Extensions 77(28)
4.1 Novel Virtual Price FAST TCP Congestion Control Approach
77(5)
4.1.1 Link Algorithm and Source Algorithm
78(1)
4.1.1.1 Link Algorithm: Virtual Pricing Approach
78(1)
4.1.1.2 Source Algorithm: Modified Form of FAST TCP
79(1)
4.1.2 Simulation Results
79(3)
4.1.3 Concluding Remarks
82(1)
4.2 Generalized FAST TCP Scheme
82(16)
4.2.1 Generalized FAST TCP
83(5)
4.2.2 Stability Analyses
88(4)
4.2.3 Fairness-Scalability Trade-Off
92(1)
4.2.4 Simulation Results
93(2)
4.2.5 Concluding Remarks
95(3)
4.3 New FAST TCP Variant Considering Packet Loss
98(7)
4.3.1 Optimization Problem
99(1)
4.3.2 New Flow-Level Model of FAST TCP
100(3)
4.3.3 Simulation Results
103(1)
4.3.4 Concluding Remarks
103(2)
Chapter 5 Fairness and Bandwidth Allocation 105(28)
5.1 General Description of Max-MM Fairness
105(1)
5.2 Max-MM and Min-Max Fairness in Euclidean Spaces
106(3)
5.2.1 Definitions and Uniqueness
106(1)
5.2.2 Max-Min Fairness and Leximin Ordering
107(1)
5.2.3 Existence and Max-MM Achievable Sets
108(1)
5.3 MP Algorithm and WF Algorithm
109(2)
5.3.1 MP Algorithm
109(1)
5.3.2 WF Algorithm
110(1)
5.4 Proportional Fairness
111(11)
5.4.1 Fast Transmission Control Protocol: Realization of Weighted PF in Bandwidth Allocation
113(1)
5.4.2 Weighted Proportional Fair and FAST TCP
113(2)
5.4.3 Proportional Fair Resource Allocation in Wireless Networks
115(7)
5.5 (p,beta)-Proportional Fairness
122(3)
5.6 Utility Fairness Index: A New Measure of Fairness for Utility-Aware Bandwidth Allocation
125(6)
5.6.1 Introduction
125(1)
5.6.2 Utility Fairness Index
126(1)
5.6.3 Numerical Examples
127(4)
5.6.4 Conclusions
131(1)
5.7 Further Discussions on Fair Sharing Resource Policies in the Context of Communication Networks
131(2)
Chapter 6 Fair Bandwidth Allocation Algorithms for Ring Metropolitan Area Networks 133(14)
6.1 Introduction
133(1)
6.2 Basic Concepts of RPR Fairness Algorithms
134(2)
6.3 AM, CM, and DVSR Algorithms
136(4)
6.3.1 Aggressive Mode and Conservative Mode
136(2)
6.3.2 AM Algorithm
138(1)
6.3.3 CM Algorithm
139(1)
6.4 DVSR Algorithm
140(7)
6.4.1 Performance Comparisons of AM, CM, and DVSR
142(5)
Chapter 7 Efficient and Fair Bandwidth Allocation for Wide and Metropolitan Area Networks 147(12)
7.1 Introduction
147(1)
7.2 Bandwidth Allocation on a Single Link
148(1)
7.3 New Weight Function for GW Fairness
149(1)
7.4 ABA Algorithm
150(7)
7.4.1 Traffic Control
150(1)
7.4.2 Description of the ABA Algorithm
150(2)
7.4.3 Stability Analyses to the ABA Algorithm
152(5)
7.5 Application of the ABA Algorithm to the Ring Metropolitan Area Networks
157(1)
7.6 Conclusions
157(2)
Chapter 8 Trade-Off Approach between the Efficiency and Fairness for Networks 159(32)
8.1 General Background on Fairness and Efficiency Trade-Off Issue
159(1)
8.2 (alpha,beta)-Fairness: General Concept for Balancing the Fairness and Efficiency
159(4)
8.3 Nonlinear Program Formulation in Terms of (alpha,beta)-Fairness
163(1)
8.4 Analyses and Solution Methodologies
164(4)
8.5 Numerical Studies
168(15)
8.5.1 Example 1: Linear Network with Uniform Capacity
169(3)
8.5.2 Example 2: Linear Network with Two Long Flows
172(4)
8.5.3 Example 3: 12-Node Network
176(4)
8.5.4 Example 4: Case with a Remote Node
180(3)
8.6 Resource Allocation in Terms of a-Fairness
183(6)
8.6.1 alpha-Fairness and an Optimization Model for Trade-Off between Total Revenue and Fairness
184(2)
8.6.2 Two Illustrating Examples
186(3)
8.7 Conclusions
189(2)
Chapter 9 Fairness Comparisons among the Leading High-Speed Protocols 191(20)
9.1 Fairness Comparison between FAST TCP and TCP Reno
191(9)
9.1.1 The Metric
192(4)
9.1.2 Fairness Comparison of FAST TCP Versus TCP Reno for a General Network
196(1)
9.1.3 Two Numerical Examples
197(2)
9.1.4 Concluding Remarks
199(1)
9.2 Fairness Comparison between FAST TCP and TCP Vegas
200(9)
9.2.1 General Background, Notations, and Models of TCP Vegas and FAST TCP
200(1)
9.2.2 Equilibrium Conditions, Utility Functions, and Persistent Congestion
201(2)
9.2.3 Comparison of the Fairness of FAST TCP and TCP Vegas
203(2)
9.2.4 Simulation Results
205(3)
9.2.5 Concluding Remarks
208(1)
9.3 Experimental Fairness Comparison among FAST TCP, HSTCP, STCP, and TCP Reno
209(2)
Chapter 10 Bidirectional Transmission and an Extension of Network Utility Maximization Model 211(12)
10.1 Introduction
211(1)
10.2 Performance of Bidirectional Flows
212(6)
10.2.1 Simple Bidirectional Model in a Single Asymmetric Bottleneck Link
213(3)
10.2.2 Complex Bidirectional in a Single-Bottleneck Link
216(2)
10.3 Extended NUM Model from One-Way Flows to Bidirectional Flows
218(1)
10.4 Two Examples of the NUM Model of Bidirectional Flows
219(4)
Chapter 11 Traffic Matrix Estimation 223(14)
11.1 Introduction
223(1)
11.2 Related Work
224(1)
11.3 Methodology and Main Results
224(4)
11.3.1 Problem Statement
224(1)
11.3.2 Prior Generating
225(1)
11.3.3 Methodology and Main Results for SVDLM
226(1)
11.3.4 SVDLM Algorithm Description
227(1)
11.3.5 Computational Complexity
227(1)
11.4 Improved Algorithm for Time-Varying Network
228(1)
11.4.1 Covariance Matrix
228(1)
11.4.2 SVDLM-I Algorithm Description for Time-Varying Network
229(1)
11.5 Numerical Results
229(6)
11.6 Conclusions
235(2)
Chapter 12 Utility-Optimized Aggregate Flow-Level Bandwidth Allocation 237(20)
12.1 Introduction
237(2)
12.2 Aggregate Flow-Level Bandwidth Allocation: General Model and General Solution
239(5)
12.3 Case of the Routing Matrix Being Full-Row Rank
244(4)
12.4 Utility Function of the Aggregate Flow
248(4)
12.5 Case of the Network with Every Link Having Single-Hop Flow
252(1)
12.6 Application to Bandwidth Provision in IP-VPN Networks
253(2)
12.7 Conclusion
255(2)
Chapter 13 Bandwidth Allocation of OBS Networks Using the Aggregate Flow Level Network Utility Maximization Approach 257(28)
13.1 Introduction
257(1)
13.2 Optical Burst Switching Network
258(3)
13.2.1 Edge Nodes of OBS Network
258(2)
13.2.2 Core Nodes of OBS Network
260
13.2.3 TCP over OBS
259(2)
13.3 Router-Level Bandwidth Allocation Approach
261(3)
13.3.1 Network Model with a Full Row Rank Routing Matrix
263(1)
13.3.2 Network Model with Every Link Having a Single-Hop Flow
264(1)
13.4 Applications to OBS Networks: Demonstrating Examples
264(14)
13.4.1 OBS Network Model with Three to Seven Nodes
265(6)
13.4.2 OBS Network Model with Four to Nine Nodes
271(7)
13.5 Numerical Plots and Analyses
278(4)
13.5.1 Summary
282(1)
13.6 Novel Algorithm for Bandwidth Allocation of OBS Networks Using the Utility Maximization Approach
282(1)
13.7 Conclusion
282(3)
Chapter 14 Power Adjusting Algorithm on Mobility Control for Mobile Ad Hoc Networks 285(16)
14.1 Introduction
285(2)
14.2 Main Background
287(1)
14.3 Propagation Model, Mobility Model, and Network Assumptions
288(2)
14.3.1 Propagation Model
288(1)
14.3.2 Mobility Model
288(1)
14.3.3 Network Assumptions
289(1)
14.4 PAA Design
290(3)
14.4.1 Description of PAA
290(1)
14.4.2 Analysis of PAA
290(2)
14.4.3 Method of Distance Estimation
292(1)
14.5 Parameters Setting of PAA
293(4)
14.5.1 Average Distance of Any Two MHs
293(1)
14.5.2 Energy Consumption on Route Discovery
294(1)
14.5.3 Distance Variety between Adjacent MHs on the Routing Path
295(1)
14.5.4 Finding the Optimal Length of the Period
296(1)
14.6 Simulation Results
297(2)
14.7 Conclusions
299(2)
Chapter 15 PCA-Guided Routing Algorithm for Wireless Sensor Networks 301(16)
15.1 Introduction
301(1)
15.2 System Model
302(1)
15.2.1 Network Model
302(1)
15.2.2 Energy Consumption Model
303(1)
15.3 PCA-Guided Routing Algorithm Model
303(5)
15.3.1 Notations
303(1)
15.3.2 PCA-Guided Clustering Model
304(3)
15.3.2.1 K-Means Clustering Model
304(1)
15.3.2.2 PCA-Guided Relaxation Model
305(1)
15.3.2.3 PCA-Guided Clustering Model
306(1)
15.3.3 PCA-Guided Data Aggregating Model
307(1)
15.4 PCA-Guided Routing Algorithm Solution Strategies
308(4)
15.4.1 Initialization Stage
308(1)
15.4.2 Clusters Splitting Stage
309(1)
15.4.3 Cluster Balancing Stage
310(1)
15.4.4 CHs Selecting Stage
311(1)
15.4.5 Data Aggregating Stage
311(1)
15.4.6 Description for PCA-Guided Routing Algorithm
311(1)
15.5 Simulation Results
312(2)
15.6 Conclusions
314(3)
Chapter 16 Wireless Sensor Networks: Optimally Configuring and Clustering Approaches 317(28)
16.1 General Background
317(1)
16.2 Novel Metric for Optimal Clustering
318(5)
16.2.1 Network Assumptions
319(1)
16.2.2 Radio Model and Energy Consumption of a CH
319(1)
16.2.3 Novel Metric: The Time Matrix
320(2)
16.2.4 Numerical Example
322(1)
16.3 DOCE
323(5)
16.3.1 Static Scenario of DOCE
324(1)
16.3.1.1 Evaluate the Network Lifetime by Solving a Min/Min Problem
324(1)
16.3.2 Dynamic Scenario of DOCE
325(1)
16.3.2.1 CH Election
325(1)
16.3.2.2 Assignments of Regular Nodes
325(1)
16.3.2.3 Evaluate Network Lifetime by Solving a Max Problem
326(1)
16.3.3 Description of DOCE
326(1)
16.3.4 Complexity Analysis
327(1)
16.4 Simulation Results of the Optimal Network Configuration Algorithm: DOCE
328(7)
16.4.1 Dynamic Scenario of DOCE
329(4)
16.4.2 Static Scenario of DOCE
333(2)
16.5 Multitier Clustered Network Topology Analysis
335(4)
16.5.1 Transmission Energy Model
336(1)
16.5.2 Energy Consumption of the Tier-i
336(1)
16.5.3 Energy Model of Multitier Clustering Scheme
337(1)
16.5.4 Energy Model Analysis
338(1)
16.6 Example of Finding Optimal Tiers of Multitier Clustering Scheme
339(1)
16.7 Distributed Multitier Cluster Algorithm
340(2)
16.7.1 Algorithm Description
340(2)
16.7.2 Time Complexity of the Clustering Algorithm
342(1)
16.8 Conclusion
342(3)
Chapter 17 Big Data Collection in Wireless Sensor Networks: Methods and Algorithms 345(22)
17.1 Introduction
345(2)
17.2 Related Work
347(2)
17.3 SSIM to Image Quality Assessment
349(1)
17.4 SFDC Framework
350(6)
17.4.1 Cluster Construction
351(1)
17.4.2 CH Selection
352(1)
17.4.3 Nodes Scheduling Scheme Based on the SSIM Index
352(3)
17.4.4 Data Collection
355(1)
17.4.5 Energy Consumption
356(1)
17.5 Performance Evaluation
356(8)
17.5.1 Real Data Set
356(4)
17.5.1.1 Correctness of Clustering with SFDC
357(1)
17.5.1.2 Fidelity without the Dynamical Adjustment of Td
358(1)
17.5.1.3 Correctness of CH and Active Nodes Selection
359(1)
17.5.2 Node Contribution Rate
360(2)
17.5.2.1 Effect of Adaptive Data Collection
361(1)
17.5.3 Synthetic Data
362(7)
17.5.3.1 Node Contribution and ANR
363(1)
17.5.3.2 Effect of Adaptive Data Collection
364(1)
17.6 Concluding Remarks
364(3)
Chapter 18 Trade-Off between Network Lifetime and Utility in Wireless Sensor Networks 367(26)
18.1 Introduction
367(2)
18.2 NUM and System Model
369(4)
18.2.1 MAC Constraints
370(1)
18.2.2 Network Utility Maximization
371(1)
18.2.3 Network Lifetime Maximization
371(2)
18.3 Partially Distributed Algorithm from Duality Decomposition
373(3)
18.3.1 Duality Problem
373(2)
18.3.2 Partially Distributed Implementation
375(1)
18.4 Fully Distributed Algorithms
376(2)
18.4.1 Subgradient-Based Algorithm
377(1)
18.4.2 Implementations
378(1)
18.5 Analyses to the Convergence of the Distributed Algorithms
378(2)
18.6 Numerical Studies and Performance Analyses
380(10)
18.6.1 Numerical Results on Convergence and the Trade-Off Obtained by Algorithm 18.1
381(3)
18.6.1.1 Convergence
381(1)
18.6.1.2 Trade-Off between the QoS and Lifetime
382(2)
18.6.2 Numerical Convergence Results of Algorithm 18.4.1
384(5)
18.6.2.1 Convergence Property
384(2)
18.6.2.2 Impact of the Trade-Off Parameter on the Network Properties
386(3)
18.6.3 Network Properties under Various Nonuniform Link Error Probabilities
389(1)
18.7 Concluding Remarks
390(3)
Chapter 19 Resource Allocation among Real-Time Multimedia Users in Wireless Networks: Approximate NUM Model and Solution 393(22)
19.1 Introduction
393(3)
19.1.1 Motivation
393(3)
19.1.2 Main Contributions and Novelty
396(1)
19.2 The Bandwidth Allocation Algorithm
396(9)
19.2.1 System Model and Problem Description
396(1)
19.2.2 Approximate Model and Solution
397(6)
19.2.3 Heuristic Resource Allocation Algorithm
403(2)
19.3 Fast Suboptimal Admission Control Protocol
405(4)
19.4 Simulation Results
409(4)
19.5 Conclusion
413(2)
Chapter 20 Resource Allocation for Hard QoS Traffic and Elastic Traffic in Wireless Networks 415(24)
20.1 Introduction
415(2)
20.2 Resource Allocation among Hard QoS Traffic and Best-Effort Traffic: Network Utility Maximization (NUM) Approach
417(8)
20.2.1 Model and Problem Statement
417(2)
20.2.2 HQ Allocation for Hard QoS Traffic
419(3)
20.2.3 Elastic Allocation for the Best-Effort Traffic
422(2)
20.2.4 Mixture of Hard QoS and Best-Effort Traffic
424(1)
20.3 Numerical Results
425(13)
20.4 Radio Resource Allocation in Wireless Networks: Implementation and Performance Optimization
438(1)
Chapter 21 Utility Optimization-Based Resource Allocation for Soft QoS Traffic 439(16)
21.1 Introduction
439(2)
21.2 Problem Description and Optimal Solution of Utility Maximization
441(8)
21.2.1 Utility Function of Soft QoS Traffic and the Utility Maximization Problem
441(3)
21.2.2 Optimal Solution to the Utility Maximization Problem
444(5)
21.3 Algorithm USQ to Obtain the Optimal Solution
449(1)
21.4 Numerical Examples
449(3)
21.5 Conclusion
452(3)
References 455(34)
Index 489
Liansheng Tan is a Professor in Computer Communications in Department of Computer Science, Central China Normal University, Wuhan, China. Hewas head of the above department from 2002 to 2009. He received his PhD degree in Mathematical Sciences from Loughborough University, UK, in 1999. He worked at Research School of Information Sciences and Engineering, The Australian National University, Australia from Sept. 2006 to Nov. 2008 as an Academic Staff. He was also working with School of Information Technology and Engineering at University of Ottawa, Canada in 2001 as a postdoctoral research fellow. He has held a number of visiting research positions at Oxford University, Loughborough University, University of Tsukuba, City University of Hong Kong and University of Melbourne.



He is the Editor-in-Chief of Journal of Computers, Editor of Journal of Computer Networks and Communications, Editor of International Journal of Wireless Communications and Mobile Computing. He has served as an Associate Editor of Dynamics of Continuous, Discrete & Impulsive Systems (Series B: Applications & Algorithms), and as an editor of International Journal of Communication Systems and numerous IEEE and ACM conferences as a TPC member. I have published over 130 referred papers widely in international journals and international conference proceedings.



His present main research interests include graph theory, performance evaluation and congestion control in computer communication networks, protocols and architecture in telecommunications networks, wireless networks and sensor networks and wireless communication and mobile computing.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97814987694572e.html
Keywords: