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The Resource Advances in computer communications and networks from green, mobile, pervasive networking to big data computing, edited by Kewei Sha, Aaron Striegel, Min Song

Advances in computer communications and networks from green, mobile, pervasive networking to big data computing, edited by Kewei Sha, Aaron Striegel, Min Song

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Advances in computer communications and networks from green, mobile, pervasive networking to big data computing
Title
Advances in computer communications and networks from green, mobile, pervasive networking to big data computing
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edited by Kewei Sha, Aaron Striegel, Min Song
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eng
Summary
Recent developments in computer communications and networks have enabled the deployment of exciting new areas such as Internet of Things and collaborative big data analysis. The design and implementation of energy efficient future generation communication and networking technologies also require the clever research and development of mobile, pervasive, and large-scale computing technologies. Advances in Computer Communications and Networks: from Green, Mobile, Pervasive Networking to Big Data Computing studies and presents recent advances in communication and networking technologies reflecting the state-of-the-art research achievements in novel communication technology and network optimization. Technical topics discussed in the book include: * Data Center Networks * Mobile Ad Hoc Networks * Multimedia Networks * Internet of Things * Wireless Spectrum * Network Optimization. This book is ideal for personnel in computer communication and networking industries as well as academic staff and collegial, master, Ph.D. students in computer science, computer engineering, electrical engineering and telecommunication systems
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CaBNVSL
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004.6
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illustrations
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index present
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  • dictionaries
  • bibliography
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1969-
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  • Striegel, Aaron
  • Song, Min
  • Sha, Kewei
  • IEEE Xplore (Online Service)
  • River Publishers
Series statement
River Publishers series in communications
http://library.link/vocab/subjectName
  • Internet of things
  • Big data
  • Information technology
  • Computer networks
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Advances in computer communications and networks from green, mobile, pervasive networking to big data computing, edited by Kewei Sha, Aaron Striegel, Min Song
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online resource
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rdacarrier
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text
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rdacontent
Contents
  • Preface xix Acknowledgments xxi -- List of Contributors xxiii -- List of Figures xxxi -- List of Tables xlv List of Algorithms xlix List of Abbreviations li PART I: Data Center Computing 1 Flyover: A Cost-Efficient and Scale-Out Data Center Network Architecture 3 Sheng Xu, Binzhang Fu, Mingyu Chen and Lixin Zhang 1.1 Introduction 4 -- 1.2 RelatedWorks and Motivation 7 -- 1.2.1 RelatedWorks 7 -- 1.2.2 Motivation 8 -- 1.3 The Flyover 9 -- 1.3.1 Overview 9 -- 1.3.2 Serpent Flow 11 -- 1.3.3 Semi-Random Heuristic Algorithm 11 -- 1.3.4 Region-to-Region On-Demand Shortcuts 13 -- 1.3.5 The Scalability of Flyover 13 -- 1.4 Simulations and Analyses 14 -- 1.4.1 The Methodology 16 -- 1.4.2 Elephant Flow vs Serpent Flow 17 -- 1.4.3 Point-to-Point vs Region-to-Region 17 -- 1.4.4 Edmonds Algorithm vs Semi-Random Heuristic Algorithm 18 -- 1.4.5 The Number of Shortcuts 19 -- 1.4.6 Reconfiguration Cost 21 -- 1.4.7 Flyover vs SWDC 23 -- 1.4.8 The Design Space 25 -- 1.4.9 The Cost Comparison 27 -- 1.5 Prototype Evaluations 28 -- 1.6 Discussion 30 -- 1.6.1 The Bandwidth of Shortcut 30 -- 1.6.2 The Sharing Region of Shortcut 30 -- 1.7 Conclusion 31 Acknowledgment 32 -- References 32 -- 2 Dynamic Power Management in Data Centres 37 Waltenegus Dargie and Franz Eichhorn 2.1 Introduction 38 -- 2.2 Analysis of DC Power 39 -- 2.3 Dynamic Voltage and Frequency Scaling 41 -- 2.4 Workload Consolidation 42 -- 2.5 The HAECubie Demonstrator 46 -- 2.5.1 Consolidation 50 -- 2.5.2 Workload Prediction 51 -- 2.5.3 Workload Generation 52 -- 2.6 HAECubie Evaluation 53 -- 2.6.1 Dealing with Underutilisation and Overloading Conditions 53 -- 2.6.2 Energy and Power Consumption 54 -- 2.6.3 Throughput 55 -- 2.6.4 Latency 56 -- 2.6.5 Summary 58 -- 2.7 Conclusion 58 Acknowledgement 59 -- References 60 PART II: Mobile Computing 3 Mitigating Bufferbloat with Receiver-based TCP Flow Control Mechanism in Cellular Networks 65 Xiaolan Liu, Fengyuan Ren, Ran Shu, Tong Zhang and Tao Dai 3.1 Introduction 66 -- 3.2 Background, RelatedWork and Motivation 68
  • Faheem 8.1 Introduction 225 8.2 SA in Mesh Networks: A Special Case of Multiprocessor Scheduling 228 8.2.1 Illustrative Example 229 8.2.2 Complexity Results 230 8.3 Scheduling Algorithms for Spectrum Assignment in Mesh Networks 231 8.3.1 Scheduling Algorithm for Chain Networks 234 8.4 Numerical Results 235 8.4.1 Mesh Networks 237 8.4.2 Chain Networks 239 8.4.3 Running Time Scalability 241 8.5 Concluding Remarks 242 Acknowledgments 242 References 242 9 Wideband Spectrum Sensing in Cognitive Radio Networks 245 Prosanta Paul, ChunSheng Xin, Min Song and Yanxiao Zhao 9.1 Hypothesis Testing 246 9.2 Single-Band Spectrum Sensing Methods 247 9.2.1 Energy Detection 248 9.2.2 Matched Filter Detection 250 9.2.3 Cyclostationary Feature Detection 251 9.2.4 Other Methods 252 9.3 Wideband or Subdivided Band Spectrum Sensing 254 9.3.1 Wavelet Transform (WT) 255 9.3.2 Signal Edge Detection Using DWT 259 9.3.3 Wideband Spectrum Sensing Using DWT 260 9.3.3.1 Spectrum sensing by WTMM 262 9.3.3.2 Spectrum sensing by WTMP and WTMS 262 9.4 Exponentially Moving Averaged Multiscale Summation (EMAMS) 263 9.4.1 Edge Detection through EMAMS 265 9.4.2 Adaptive Thresholds 266 9.4.3 EMAMS Algorithm 268 9.5 Performance Evaluation of EMAMS 268 9.6 Summary 272 References 274 PART IV: Pervasive Computing/Sensor Networks/IoT 10 Assessing Performance of Smart Grid Applications Using Co-simulation 282 Paul Moulema,Wei Yu, Sriharsha Mallapuram, David Griffith and Nada Golmie 10.1 Introduction 282 10.2 Background and RelatedWork 283 10.3 Co-simulation Models and Scenarios 286 10.3.1 Power Grid and Communication Network Models 286 10.3.2 Co-simulation Scenarios 286 10.3.2.1 Smart Grid applications 289 10.3.2.2 Operation conditions 289 10.3.2.3 Co-simulation scenarios 290 10.3.3 Discussion 292 10.4 Performance Evaluation 292 10.4.1 Demand Response 293 10.4.2 Energy Market: Market Clearing Price 298 10.4.3 Energy Market: Market Clearing Quantity 300 10.4.4 HVAC Population Statistics 303 10.5 Extension 307 10.5.1 Wireless Network Models 308 10.5.2 Evaluation Results 308 10.6 Conclusion 313 References 313 11 Tight Bounds on Localized Sensor Self-Deployment for Focused Coverage 319 Gokarna Sharma and Hari Krishnan 11.1 Introduction 320 11.1.1 Chapter Organization 322 11.2 RelatedWork 322 11.3 Model and Preliminaries 323 11.4 The Algorithm 326 11.5 The Lower Bound 327 11.6 Analysis of the TTGREEDY Algorithm 334 11.7 Experiments 337 11.8 Conclusions 340 References 341 12 Toward Resident Behavior Prediction inWireless Sensor Network-Based Smart Homes 345 Christopher Osiegbu, Seifemichael B. Amsalu, Fatemeh Afghah, Daniel Limbrick and Abdollah Homaifar 12.1 Introduction 346 12.2 RelatedWork 347 12.3 System Design 349 12.4 Test Bed 351 12.4.1 Data Gathering 352 12.5 Software 354 12.5.1 Data Classification 354 12.5.2 Support Vector Machines 355 12.5.3 Prediction 357 12.6 Results 358 12.6.1 Classification 358 12.6.2 Prediction 358 12.7 Conclusion 359 Acknowledgement 360 References 360 13 Mobile Node Scheduling in MANETs for Resource Assignment: From Hospital Assignment to Energy Charging 363 Peng Liu, Biao Xu, Zhen Jiang and JieWu 13.1 Introduction 364 13.2 Target Problem and RelatedWorks 365 13.3 System Model 367 13.4 Method to Solve Multidimension Hospital Assignment 370 13.4.1 Cost Matrix Buildup 370 13.4.2 Hospital Assignment 372 13.4.3 Parameter Formulation 376 13.5 Experimental Evaluation and Scenario Overview 378 13.6 Charger Assignment in MANETs 385 13.6.1 Charger Assignment Problem in MANETs 386 13.6.1.1 Capacity of chargers 387 13.6.1.2 Effective charging distance 387 13.6.1.3 Mobility of chargers 387 13.6.1.4 Charging duration 387 13.6.1.5 Appearance of charging request 388 13.6.1.6 Local waiting queue 388 13.6.1.7 Reservation 388 13.6.2 Bipartite Matching-Based Algorithm 389 13.6.3 Results Analysis and Discussion 389 13.7 Conclusions 390 Acknowledgment 390 References 390 PART V: Multimedia Networks 14 User Experience Awareness Network Optimization for Video Streaming Based Applications 395 Hengky Susanto, ByungGuk Kim and Benyuan Liu 14.1 Introduction 396 14.2 RelatedWork 397 14.3 Multi-Layered User Utility Function 398 14.3.1 Foundations 398 14.3.2 User Utility Function 399 14.3.3 System Setup 401 14.4 Adaptive User Demand 402 14.4.1 Adaptive Demand 402 14.4.2 User's Desire for Better Quality 403 14.4.3 The Impact of Adaptive User Demand 404 14.4.4 The Ripple Effects of Active Users on Network 407 14.5 Admission Control 408 14.5.1 Admission Control Designed 408 14.5.2 Convergence 410 14.6 Simulation and Discussion 412 14.7 In Practice 417 14.8 Conclusion 422 Acknowledgement 422 References 422 15 METhoD:AFramework for the Emulation of a Delay-Tolerant Network Scenario for Media Content Distribution in Under-Served Regions 427 Adriano Galati, Sandra Siby, Theodoros Bourchas, Maria Olivares, Stefan Mangold and Thomas R. Gross 15.1 Introduction 428 15.2 MOSAIC 2B Overview 429 15.3 Delay-Tolerant Networking 433 15.4 DTN-Enabled Infostation 434 15.5 Cinema-in-a-Backpack Kit 436 15.6 METhoD Framework 436 15.6.1 Trace Generator 439 15.6.2 Mobility Trace Processor 440 15.6.3 Switching Module 440 15.6.4 Visualizer 440 15.7 Validation 440 15.8 MOSAIC 2B Emulation 444 15.8.1 Experimental Setting 444 15.8.2 Emulation with a Single Movie 446 15.8.3 Emulation with Multiple Movies 450 15.9 RelatedWork 452 15.10 Conclusion 454 Acknowledgement 455 References 455 PART VI: Network Optimization 16 On the Routing of Kademlia-type Systems 461 Stefanie Roos, Hani Salah and Thorsten Strufe 16.1 Introduction 461 16.2 Kademlia-type Systems 463 16.2.1 Introducing Kademlia 463 16.2.2 Analyzing P2P Routing 464 16.3 Model 466 16.3.1 Assumptions 467 16.3.2 Model Overview 468 16.3.3 Distribution of Closest Contacts 470 16.3.4 Derivation of I 473 16.3.5 Derivation of T 473 16.3.6 Summary 477 16.4 Model Complexity 477 16.4.1 Space Complexity 477 16.4.2 Computation Complexity 478 16.4.3 Reducing the ID Space Size 480 16.5 Verification and Scalability 483 16.5.1 Model Verification 483 16.5.2 Scalability 486 16.5.3 Real-World Measurements 487 16.6 Extending the Model 487 16.7 Lessons Learned 489 16.8 Conclusion 491 References 492 17 Access Efficient Bloom Filters with TinySet 495 Gil Einziger and Roy Friedman 17.1 Introduction 495 17.1.1 Our Contribution 497 17.2 Background and RelatedWork 498 17.2.1 Bloom Filter Variants 498 17.2.2 Hash Table-Based Bloom Filters 499 17.3 TinySet: Dynamic Fingerprint Resizing 500 17.3.1 Motivation and Overview 500 17.3.2 Basic Block Structure 501 17.3.3 Variable Fingerprint Size 504 17.3.4 Two Fingerprint Sizes in One Block 506 17.3.5 Removing Items 507 17.3.6 Implicit Size Counters 508 17.3.7 Integration with TinyTable 509 17.3.8 Final Overview 510 17.4 Analysis 511 17.4.1 Memory Overheads 511 17.4.2 Variable-Sized Fingerprints 512 17.4.3 Variable-Sized Fingerprint with Mod 512 17.4.4 Overflows 513 17.5 Results 513 17.5.1 Operation Speed 514 17.5.2 Space/Accuracy Tradeoff 516 17.5.3 Flexibility 516 17.5.4 Removals 518 17.5.5 Integration with TinyTable 519 17.6 Conclusions and Discussion 520 References 521 18 Maximum Correntropy-Based Distributed Estimation of Adaptive Networks 525 Amir Rastegarnia, Azam Khalili,Wael M.
  • Bazzi and Saeid Sanei 18.1 Introduction 526 18.2 Background 528 18.2.1 Cooperative Strategies 528 18.2.2 Correntropy 531 18.2.3 Impulsive Noise Model 532 18.3 Derivation of Adaptive Networks under MCC 532 18.3.1 Incremental MCC Algorithm 532 18.3.2 Diffusion MCC Algorithms 534 18.4 Simulation Results 536 18.4.1 Experiment 1 536 18.4.2 Experiment 2 540 18.5 Conclusion 541 References 542 19 InfoMax: ATransport-Layer Paradigm for the Age of Data Overload 547 Jongdeog Lee, Akash Kapoor, Md Tanvir Al Amin, Zeyuan Zhang, Radhika Goyal, Tarek Abdelzaher, ZhehaoWang and Ilya Moiseenko 19.1 Introduction 548 19.2 Design and Implementation 551 19.2.1 The InfoMax Information Summarization Abstraction 551 19.2.2 Assumptions and Properties 553 19.2.3 The InfoMax Protocol 554 19.2.3.1 Producer and consumer APIs in NDN 555 19.2.3.2 Enforcing the InfoMax order 556 19.2.3.3 Handling dynamic updates 557 19.2.4 An Approximate Transmission Ordering Algorithm 559 19.2.5 Customizing InfoMax Order 562 19.3 Evaluation 564 19.3.1 Transmission Overhead 564 19.3.2 Scaling Delivery 566 19.3.3 Shortest-Shared-Postfix-First Ordering 567 19.3.4 Customized Ordering 569 19.4 Application Examples 570 19.4.1 Visual Tourism 572 19.4.2 Twitter Search 574 19.5 RelatedWork 577 19.6 Conclusions and FutureWork 581 References 581 20 Improvement in Load Balancing Decision for Massively Multiplayer Online Game (MMOG) Servers Using Markov Chains 585 Aamir Saeed, Rasmus Lvenstein Olsen and Jens Myrup Pedersen 20.1 Introduction 585 20.1.1 Hotspot Problem in MMOG 586 20.1.2 Load Balancing Approaches 586 20.1.3 Sharing of Outdated Information 588 20.1.4 Load Balancing Decision Affects Player Response Time 588 20.2 Minimizing the Impact of Outdated Information 589 20.2.1 Prediction Algorithm 589 20.2.2 Accuracy of Arrival (ˆn) and Departure Rates (ˆo) Estimates 590 20.2.3 Use Case Scenarios 591 20.2.4 Results 592 20.3 MMOG Server Load Migration Affects User Experience 594 20.3.1 System Model and Impact of Migration Decision on a User Response Time 595 20.3.1.1 Impact of Migration on User Response Time 595 20.3.1.2 Simulation and Results 596 20.4 Conclusion 599 References 600 Index 603
  • 3.2.1 Background 68 -- 3.2.1.1 Cellular networks 68 -- 3.2.1.2 Bufferbloat 69 -- 3.2.1.3 TCP flow control mechanism 70 -- 3.2.1.4 Available bandwidth 70 -- 3.2.2 RelatedWorks 70 -- 3.2.2.1 Bufferbloat 70 -- 3.2.2.2 Receiver-side flow control in cellular networks 71 -- 3.2.3 Motivation 71 -- 3.3 Algorithm Analysis and Design 72 -- 3.3.1 Retrieving the Available Bandwidth 73 -- 3.3.2 RTT Estimation 75 -- 3.3.3 Rwnd Calculation 75 -- 3.4 Simulation Configurations 76 -- 3.5 Experimental Results and Performance Evaluation 78 -- 3.5.1 TCP Performance Analysis Under Bufferbloated Circumstance 79 -- 3.5.1.1 TCP performance of ABRWDA in a bufferbloated circumstance 79 -- 3.5.1.2 TCP performance of DRWA in a bufferbloated circumstance 81 -- 3.5.1.3 TCP performance of vegas in a bufferbloated circumstance 81 -- 3.5.2 The Effect on TCP Performance Caused by Parameters' Selection 81 -- 3.5.3 The Improvement in User Experiences 83 -- 3.5.4 The Improvement of System Performance from Kalman Filter 86 -- 3.6 Conclusion 87 Acknowledgment 87 -- References 87 -- 4 Adaptive Monitoring for Mobile Networks in Challenging Environments 91 Nils Richerzhagen, BjŠ orn Richerzhagen, Rhaban Hark, Dominik Stingl, Andreas Mauthe, Alberto E. Schaeffer-Filho, Klara Nahrstedt and Ralf Steinmetz 4.1 Introduction 92 -- 4.2 Background on Monitoring in Mobile Networks 94 -- 4.2.1 Measurement 96 -- 4.2.2 Data Collection 96 -- 4.2.3 Data Analysis 97 -- 4.2.4 Information Distribution 98 -- 4.3 RelatedWork: Data Collection in Mobile Networks 98 -- 4.4 Scenario 100 -- 4.5 CRATER: Design of an Adaptive Monitoring Solution 102 -- 4.5.1 No-Sink Advertising 105 -- 4.5.2 Sink Advertising 107 -- 4.5.3 Data Routing 110 -- 4.5.4 CRATER Cloud Component 111 -- 4.6 Evaluation 111 -- 4.6.1 Modeling of the Scenario and Evaluation Setup 112 -- 4.6.2 System Parameter Configurations 114 -- 4.6.3 Robustness 117 -- 4.6.4 Static Monitoring vs. CRATER 120 -- 4.7 Conclusion 122 Acknowledgment 123 -- References 123 -- 5 Inferring Network Topologies in MANETs: Application to Service Redeployment 127 S. Silvestri, B. Holbert, P. Novotny, T. La Porta, A. Wolf and A. Swami Acknowledgement 127
  • 5.1 Introduction 128 -- 5.2 RelatedWork 129 -- 5.3 Network Model 130 -- 5.4 M-iTop Approach 131 -- 5.4.1 Virtual Topology Construction 132 -- 5.4.2 Merge Options 134 -- 5.4.3 Merging Links 135 -- 5.4.4 Inference of Nodes Physical Locations 136 -- 5.5 Iterative Service Redeployment (iSP) Algorithm 136 -- 5.5.1 Formalization of the Multiple Service Replicas Deployment Problem 137 -- 5.5.2 The iSR Algorithm 138 -- 5.6 Results 139 -- 5.6.1 First Set of Experiments 142 -- 5.6.1.1 Single connected component 142 -- 5.6.1.2 Multiple connected components 144 -- 5.6.2 Second Set of Experiments 145 -- 5.6.3 Third Set of Experiments 148 -- 5.6.4 Fourth Set of Experiments 150 -- 5.7 Discussion and Future Research Directions 151 -- 5.8 Conclusions 152 -- References 152 -- 6 Towards UnifiedWireless Network: A Software Defined Architecture based on Network Virtualization and Distributed Mobility Management 155 Jyotirmoy Banik, Marco Tacca, Andrea Fumagalli, Behcet Sarikaya and Li Xue 6.1 Introduction 156 -- 6.2 Software-Defined Distributed Mobility Management 158 -- 6.2.1 Distributed Mobility Management in Mobile Backhaul Network 158 -- 6.2.2 Virtualized Core Network Architecture 160 -- 6.2.3 Path Establishment and Packet Flow in the Core Network 161 -- 6.2.3.1 Initial attachment and session establishment 161 -- 6.2.3.2 Uplink (UE→PDN) packet flow 161 -- 6.2.3.3 Downlink (PDN→UE) packet flow 162 -- 6.2.3.4 Some key points of design 162 -- 6.2.3.5 Mobility events 163 -- 6.2.3.6 Multiple border router scenario 163 -- 6.2.3.7 IP address assignment considerations 166 -- 6.3 Analytical Modeling of Signaling Load on EPC 166 -- 6.4 Experiments and Results 169 -- 6.4.1 Test Bed Description 169 -- 6.4.2 Experiment Setup 170 -- 6.4.3 Results 172 -- 6.4.3.1 UDP traffic 172 -- 6.4.3.2 TCP traffic 172 -- 6.5 Extending the Design to Support Fixed WLAN Users 174 -- 6.5.1 Network Architecture 174 -- 6.5.1.1 Uplink (UE→PDN) packet flow 176 -- 6.5.1.2 Downlink (PDN→UE) packet flow 177
  • 6.5.2 Handling Mobility 177 -- 6.6 Conclusion 178 -- References 179 -- 7 Improving the Effectiveness of Data Transfers in Mobile Computing Using Lossless Compression Utilities 181 Armen Dzhagaryan, Aleksandar Milenković and Martin Burtscher 7.1 Introduction 182 -- 7.2 Lossless Compression Utilities 185 -- 7.3 Experimental Setup 187 -- 7.3.1 Smartphone 187 -- 7.3.2 Measurement Setup 188 -- 7.3.3 Datasets 193 -- 7.4 Metrics and Experiments 194 -- 7.4.1 Metrics 194 -- 7.4.2 Experiments 195 -- 7.5 Results 197 -- 7.5.1 Compression Ratio 198 -- 7.5.2 Compression and Decompression Throughputs 198 -- 7.5.3 Energy Efficiency 204 -- 7.5.4 Putting It All Together 210 -- 7.6 RelatedWork 216 -- 7.7 Conclusions 217 -- References 219 PART III: Spectrum 8 Scheduling-Inspired Spectrum Assignment Algorithms for Mesh Elastic Optical Networks 225 Mahmoud Fayez, Iyad Katib, George N. Rouskas and Hossam M.
  • About the Editors 607
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Advances in computer communications and networks from green, mobile, pervasive networking to big data computing, edited by Kewei Sha, Aaron Striegel, Min Song
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online resource
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rdacarrier
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Contents
  • Preface xix Acknowledgments xxi -- List of Contributors xxiii -- List of Figures xxxi -- List of Tables xlv List of Algorithms xlix List of Abbreviations li PART I: Data Center Computing 1 Flyover: A Cost-Efficient and Scale-Out Data Center Network Architecture 3 Sheng Xu, Binzhang Fu, Mingyu Chen and Lixin Zhang 1.1 Introduction 4 -- 1.2 RelatedWorks and Motivation 7 -- 1.2.1 RelatedWorks 7 -- 1.2.2 Motivation 8 -- 1.3 The Flyover 9 -- 1.3.1 Overview 9 -- 1.3.2 Serpent Flow 11 -- 1.3.3 Semi-Random Heuristic Algorithm 11 -- 1.3.4 Region-to-Region On-Demand Shortcuts 13 -- 1.3.5 The Scalability of Flyover 13 -- 1.4 Simulations and Analyses 14 -- 1.4.1 The Methodology 16 -- 1.4.2 Elephant Flow vs Serpent Flow 17 -- 1.4.3 Point-to-Point vs Region-to-Region 17 -- 1.4.4 Edmonds Algorithm vs Semi-Random Heuristic Algorithm 18 -- 1.4.5 The Number of Shortcuts 19 -- 1.4.6 Reconfiguration Cost 21 -- 1.4.7 Flyover vs SWDC 23 -- 1.4.8 The Design Space 25 -- 1.4.9 The Cost Comparison 27 -- 1.5 Prototype Evaluations 28 -- 1.6 Discussion 30 -- 1.6.1 The Bandwidth of Shortcut 30 -- 1.6.2 The Sharing Region of Shortcut 30 -- 1.7 Conclusion 31 Acknowledgment 32 -- References 32 -- 2 Dynamic Power Management in Data Centres 37 Waltenegus Dargie and Franz Eichhorn 2.1 Introduction 38 -- 2.2 Analysis of DC Power 39 -- 2.3 Dynamic Voltage and Frequency Scaling 41 -- 2.4 Workload Consolidation 42 -- 2.5 The HAECubie Demonstrator 46 -- 2.5.1 Consolidation 50 -- 2.5.2 Workload Prediction 51 -- 2.5.3 Workload Generation 52 -- 2.6 HAECubie Evaluation 53 -- 2.6.1 Dealing with Underutilisation and Overloading Conditions 53 -- 2.6.2 Energy and Power Consumption 54 -- 2.6.3 Throughput 55 -- 2.6.4 Latency 56 -- 2.6.5 Summary 58 -- 2.7 Conclusion 58 Acknowledgement 59 -- References 60 PART II: Mobile Computing 3 Mitigating Bufferbloat with Receiver-based TCP Flow Control Mechanism in Cellular Networks 65 Xiaolan Liu, Fengyuan Ren, Ran Shu, Tong Zhang and Tao Dai 3.1 Introduction 66 -- 3.2 Background, RelatedWork and Motivation 68
  • Faheem 8.1 Introduction 225 8.2 SA in Mesh Networks: A Special Case of Multiprocessor Scheduling 228 8.2.1 Illustrative Example 229 8.2.2 Complexity Results 230 8.3 Scheduling Algorithms for Spectrum Assignment in Mesh Networks 231 8.3.1 Scheduling Algorithm for Chain Networks 234 8.4 Numerical Results 235 8.4.1 Mesh Networks 237 8.4.2 Chain Networks 239 8.4.3 Running Time Scalability 241 8.5 Concluding Remarks 242 Acknowledgments 242 References 242 9 Wideband Spectrum Sensing in Cognitive Radio Networks 245 Prosanta Paul, ChunSheng Xin, Min Song and Yanxiao Zhao 9.1 Hypothesis Testing 246 9.2 Single-Band Spectrum Sensing Methods 247 9.2.1 Energy Detection 248 9.2.2 Matched Filter Detection 250 9.2.3 Cyclostationary Feature Detection 251 9.2.4 Other Methods 252 9.3 Wideband or Subdivided Band Spectrum Sensing 254 9.3.1 Wavelet Transform (WT) 255 9.3.2 Signal Edge Detection Using DWT 259 9.3.3 Wideband Spectrum Sensing Using DWT 260 9.3.3.1 Spectrum sensing by WTMM 262 9.3.3.2 Spectrum sensing by WTMP and WTMS 262 9.4 Exponentially Moving Averaged Multiscale Summation (EMAMS) 263 9.4.1 Edge Detection through EMAMS 265 9.4.2 Adaptive Thresholds 266 9.4.3 EMAMS Algorithm 268 9.5 Performance Evaluation of EMAMS 268 9.6 Summary 272 References 274 PART IV: Pervasive Computing/Sensor Networks/IoT 10 Assessing Performance of Smart Grid Applications Using Co-simulation 282 Paul Moulema,Wei Yu, Sriharsha Mallapuram, David Griffith and Nada Golmie 10.1 Introduction 282 10.2 Background and RelatedWork 283 10.3 Co-simulation Models and Scenarios 286 10.3.1 Power Grid and Communication Network Models 286 10.3.2 Co-simulation Scenarios 286 10.3.2.1 Smart Grid applications 289 10.3.2.2 Operation conditions 289 10.3.2.3 Co-simulation scenarios 290 10.3.3 Discussion 292 10.4 Performance Evaluation 292 10.4.1 Demand Response 293 10.4.2 Energy Market: Market Clearing Price 298 10.4.3 Energy Market: Market Clearing Quantity 300 10.4.4 HVAC Population Statistics 303 10.5 Extension 307 10.5.1 Wireless Network Models 308 10.5.2 Evaluation Results 308 10.6 Conclusion 313 References 313 11 Tight Bounds on Localized Sensor Self-Deployment for Focused Coverage 319 Gokarna Sharma and Hari Krishnan 11.1 Introduction 320 11.1.1 Chapter Organization 322 11.2 RelatedWork 322 11.3 Model and Preliminaries 323 11.4 The Algorithm 326 11.5 The Lower Bound 327 11.6 Analysis of the TTGREEDY Algorithm 334 11.7 Experiments 337 11.8 Conclusions 340 References 341 12 Toward Resident Behavior Prediction inWireless Sensor Network-Based Smart Homes 345 Christopher Osiegbu, Seifemichael B. Amsalu, Fatemeh Afghah, Daniel Limbrick and Abdollah Homaifar 12.1 Introduction 346 12.2 RelatedWork 347 12.3 System Design 349 12.4 Test Bed 351 12.4.1 Data Gathering 352 12.5 Software 354 12.5.1 Data Classification 354 12.5.2 Support Vector Machines 355 12.5.3 Prediction 357 12.6 Results 358 12.6.1 Classification 358 12.6.2 Prediction 358 12.7 Conclusion 359 Acknowledgement 360 References 360 13 Mobile Node Scheduling in MANETs for Resource Assignment: From Hospital Assignment to Energy Charging 363 Peng Liu, Biao Xu, Zhen Jiang and JieWu 13.1 Introduction 364 13.2 Target Problem and RelatedWorks 365 13.3 System Model 367 13.4 Method to Solve Multidimension Hospital Assignment 370 13.4.1 Cost Matrix Buildup 370 13.4.2 Hospital Assignment 372 13.4.3 Parameter Formulation 376 13.5 Experimental Evaluation and Scenario Overview 378 13.6 Charger Assignment in MANETs 385 13.6.1 Charger Assignment Problem in MANETs 386 13.6.1.1 Capacity of chargers 387 13.6.1.2 Effective charging distance 387 13.6.1.3 Mobility of chargers 387 13.6.1.4 Charging duration 387 13.6.1.5 Appearance of charging request 388 13.6.1.6 Local waiting queue 388 13.6.1.7 Reservation 388 13.6.2 Bipartite Matching-Based Algorithm 389 13.6.3 Results Analysis and Discussion 389 13.7 Conclusions 390 Acknowledgment 390 References 390 PART V: Multimedia Networks 14 User Experience Awareness Network Optimization for Video Streaming Based Applications 395 Hengky Susanto, ByungGuk Kim and Benyuan Liu 14.1 Introduction 396 14.2 RelatedWork 397 14.3 Multi-Layered User Utility Function 398 14.3.1 Foundations 398 14.3.2 User Utility Function 399 14.3.3 System Setup 401 14.4 Adaptive User Demand 402 14.4.1 Adaptive Demand 402 14.4.2 User's Desire for Better Quality 403 14.4.3 The Impact of Adaptive User Demand 404 14.4.4 The Ripple Effects of Active Users on Network 407 14.5 Admission Control 408 14.5.1 Admission Control Designed 408 14.5.2 Convergence 410 14.6 Simulation and Discussion 412 14.7 In Practice 417 14.8 Conclusion 422 Acknowledgement 422 References 422 15 METhoD:AFramework for the Emulation of a Delay-Tolerant Network Scenario for Media Content Distribution in Under-Served Regions 427 Adriano Galati, Sandra Siby, Theodoros Bourchas, Maria Olivares, Stefan Mangold and Thomas R. Gross 15.1 Introduction 428 15.2 MOSAIC 2B Overview 429 15.3 Delay-Tolerant Networking 433 15.4 DTN-Enabled Infostation 434 15.5 Cinema-in-a-Backpack Kit 436 15.6 METhoD Framework 436 15.6.1 Trace Generator 439 15.6.2 Mobility Trace Processor 440 15.6.3 Switching Module 440 15.6.4 Visualizer 440 15.7 Validation 440 15.8 MOSAIC 2B Emulation 444 15.8.1 Experimental Setting 444 15.8.2 Emulation with a Single Movie 446 15.8.3 Emulation with Multiple Movies 450 15.9 RelatedWork 452 15.10 Conclusion 454 Acknowledgement 455 References 455 PART VI: Network Optimization 16 On the Routing of Kademlia-type Systems 461 Stefanie Roos, Hani Salah and Thorsten Strufe 16.1 Introduction 461 16.2 Kademlia-type Systems 463 16.2.1 Introducing Kademlia 463 16.2.2 Analyzing P2P Routing 464 16.3 Model 466 16.3.1 Assumptions 467 16.3.2 Model Overview 468 16.3.3 Distribution of Closest Contacts 470 16.3.4 Derivation of I 473 16.3.5 Derivation of T 473 16.3.6 Summary 477 16.4 Model Complexity 477 16.4.1 Space Complexity 477 16.4.2 Computation Complexity 478 16.4.3 Reducing the ID Space Size 480 16.5 Verification and Scalability 483 16.5.1 Model Verification 483 16.5.2 Scalability 486 16.5.3 Real-World Measurements 487 16.6 Extending the Model 487 16.7 Lessons Learned 489 16.8 Conclusion 491 References 492 17 Access Efficient Bloom Filters with TinySet 495 Gil Einziger and Roy Friedman 17.1 Introduction 495 17.1.1 Our Contribution 497 17.2 Background and RelatedWork 498 17.2.1 Bloom Filter Variants 498 17.2.2 Hash Table-Based Bloom Filters 499 17.3 TinySet: Dynamic Fingerprint Resizing 500 17.3.1 Motivation and Overview 500 17.3.2 Basic Block Structure 501 17.3.3 Variable Fingerprint Size 504 17.3.4 Two Fingerprint Sizes in One Block 506 17.3.5 Removing Items 507 17.3.6 Implicit Size Counters 508 17.3.7 Integration with TinyTable 509 17.3.8 Final Overview 510 17.4 Analysis 511 17.4.1 Memory Overheads 511 17.4.2 Variable-Sized Fingerprints 512 17.4.3 Variable-Sized Fingerprint with Mod 512 17.4.4 Overflows 513 17.5 Results 513 17.5.1 Operation Speed 514 17.5.2 Space/Accuracy Tradeoff 516 17.5.3 Flexibility 516 17.5.4 Removals 518 17.5.5 Integration with TinyTable 519 17.6 Conclusions and Discussion 520 References 521 18 Maximum Correntropy-Based Distributed Estimation of Adaptive Networks 525 Amir Rastegarnia, Azam Khalili,Wael M.
  • Bazzi and Saeid Sanei 18.1 Introduction 526 18.2 Background 528 18.2.1 Cooperative Strategies 528 18.2.2 Correntropy 531 18.2.3 Impulsive Noise Model 532 18.3 Derivation of Adaptive Networks under MCC 532 18.3.1 Incremental MCC Algorithm 532 18.3.2 Diffusion MCC Algorithms 534 18.4 Simulation Results 536 18.4.1 Experiment 1 536 18.4.2 Experiment 2 540 18.5 Conclusion 541 References 542 19 InfoMax: ATransport-Layer Paradigm for the Age of Data Overload 547 Jongdeog Lee, Akash Kapoor, Md Tanvir Al Amin, Zeyuan Zhang, Radhika Goyal, Tarek Abdelzaher, ZhehaoWang and Ilya Moiseenko 19.1 Introduction 548 19.2 Design and Implementation 551 19.2.1 The InfoMax Information Summarization Abstraction 551 19.2.2 Assumptions and Properties 553 19.2.3 The InfoMax Protocol 554 19.2.3.1 Producer and consumer APIs in NDN 555 19.2.3.2 Enforcing the InfoMax order 556 19.2.3.3 Handling dynamic updates 557 19.2.4 An Approximate Transmission Ordering Algorithm 559 19.2.5 Customizing InfoMax Order 562 19.3 Evaluation 564 19.3.1 Transmission Overhead 564 19.3.2 Scaling Delivery 566 19.3.3 Shortest-Shared-Postfix-First Ordering 567 19.3.4 Customized Ordering 569 19.4 Application Examples 570 19.4.1 Visual Tourism 572 19.4.2 Twitter Search 574 19.5 RelatedWork 577 19.6 Conclusions and FutureWork 581 References 581 20 Improvement in Load Balancing Decision for Massively Multiplayer Online Game (MMOG) Servers Using Markov Chains 585 Aamir Saeed, Rasmus Lvenstein Olsen and Jens Myrup Pedersen 20.1 Introduction 585 20.1.1 Hotspot Problem in MMOG 586 20.1.2 Load Balancing Approaches 586 20.1.3 Sharing of Outdated Information 588 20.1.4 Load Balancing Decision Affects Player Response Time 588 20.2 Minimizing the Impact of Outdated Information 589 20.2.1 Prediction Algorithm 589 20.2.2 Accuracy of Arrival (ˆn) and Departure Rates (ˆo) Estimates 590 20.2.3 Use Case Scenarios 591 20.2.4 Results 592 20.3 MMOG Server Load Migration Affects User Experience 594 20.3.1 System Model and Impact of Migration Decision on a User Response Time 595 20.3.1.1 Impact of Migration on User Response Time 595 20.3.1.2 Simulation and Results 596 20.4 Conclusion 599 References 600 Index 603
  • 3.2.1 Background 68 -- 3.2.1.1 Cellular networks 68 -- 3.2.1.2 Bufferbloat 69 -- 3.2.1.3 TCP flow control mechanism 70 -- 3.2.1.4 Available bandwidth 70 -- 3.2.2 RelatedWorks 70 -- 3.2.2.1 Bufferbloat 70 -- 3.2.2.2 Receiver-side flow control in cellular networks 71 -- 3.2.3 Motivation 71 -- 3.3 Algorithm Analysis and Design 72 -- 3.3.1 Retrieving the Available Bandwidth 73 -- 3.3.2 RTT Estimation 75 -- 3.3.3 Rwnd Calculation 75 -- 3.4 Simulation Configurations 76 -- 3.5 Experimental Results and Performance Evaluation 78 -- 3.5.1 TCP Performance Analysis Under Bufferbloated Circumstance 79 -- 3.5.1.1 TCP performance of ABRWDA in a bufferbloated circumstance 79 -- 3.5.1.2 TCP performance of DRWA in a bufferbloated circumstance 81 -- 3.5.1.3 TCP performance of vegas in a bufferbloated circumstance 81 -- 3.5.2 The Effect on TCP Performance Caused by Parameters' Selection 81 -- 3.5.3 The Improvement in User Experiences 83 -- 3.5.4 The Improvement of System Performance from Kalman Filter 86 -- 3.6 Conclusion 87 Acknowledgment 87 -- References 87 -- 4 Adaptive Monitoring for Mobile Networks in Challenging Environments 91 Nils Richerzhagen, BjŠ orn Richerzhagen, Rhaban Hark, Dominik Stingl, Andreas Mauthe, Alberto E. Schaeffer-Filho, Klara Nahrstedt and Ralf Steinmetz 4.1 Introduction 92 -- 4.2 Background on Monitoring in Mobile Networks 94 -- 4.2.1 Measurement 96 -- 4.2.2 Data Collection 96 -- 4.2.3 Data Analysis 97 -- 4.2.4 Information Distribution 98 -- 4.3 RelatedWork: Data Collection in Mobile Networks 98 -- 4.4 Scenario 100 -- 4.5 CRATER: Design of an Adaptive Monitoring Solution 102 -- 4.5.1 No-Sink Advertising 105 -- 4.5.2 Sink Advertising 107 -- 4.5.3 Data Routing 110 -- 4.5.4 CRATER Cloud Component 111 -- 4.6 Evaluation 111 -- 4.6.1 Modeling of the Scenario and Evaluation Setup 112 -- 4.6.2 System Parameter Configurations 114 -- 4.6.3 Robustness 117 -- 4.6.4 Static Monitoring vs. CRATER 120 -- 4.7 Conclusion 122 Acknowledgment 123 -- References 123 -- 5 Inferring Network Topologies in MANETs: Application to Service Redeployment 127 S. Silvestri, B. Holbert, P. Novotny, T. La Porta, A. Wolf and A. Swami Acknowledgement 127
  • 5.1 Introduction 128 -- 5.2 RelatedWork 129 -- 5.3 Network Model 130 -- 5.4 M-iTop Approach 131 -- 5.4.1 Virtual Topology Construction 132 -- 5.4.2 Merge Options 134 -- 5.4.3 Merging Links 135 -- 5.4.4 Inference of Nodes Physical Locations 136 -- 5.5 Iterative Service Redeployment (iSP) Algorithm 136 -- 5.5.1 Formalization of the Multiple Service Replicas Deployment Problem 137 -- 5.5.2 The iSR Algorithm 138 -- 5.6 Results 139 -- 5.6.1 First Set of Experiments 142 -- 5.6.1.1 Single connected component 142 -- 5.6.1.2 Multiple connected components 144 -- 5.6.2 Second Set of Experiments 145 -- 5.6.3 Third Set of Experiments 148 -- 5.6.4 Fourth Set of Experiments 150 -- 5.7 Discussion and Future Research Directions 151 -- 5.8 Conclusions 152 -- References 152 -- 6 Towards UnifiedWireless Network: A Software Defined Architecture based on Network Virtualization and Distributed Mobility Management 155 Jyotirmoy Banik, Marco Tacca, Andrea Fumagalli, Behcet Sarikaya and Li Xue 6.1 Introduction 156 -- 6.2 Software-Defined Distributed Mobility Management 158 -- 6.2.1 Distributed Mobility Management in Mobile Backhaul Network 158 -- 6.2.2 Virtualized Core Network Architecture 160 -- 6.2.3 Path Establishment and Packet Flow in the Core Network 161 -- 6.2.3.1 Initial attachment and session establishment 161 -- 6.2.3.2 Uplink (UE→PDN) packet flow 161 -- 6.2.3.3 Downlink (PDN→UE) packet flow 162 -- 6.2.3.4 Some key points of design 162 -- 6.2.3.5 Mobility events 163 -- 6.2.3.6 Multiple border router scenario 163 -- 6.2.3.7 IP address assignment considerations 166 -- 6.3 Analytical Modeling of Signaling Load on EPC 166 -- 6.4 Experiments and Results 169 -- 6.4.1 Test Bed Description 169 -- 6.4.2 Experiment Setup 170 -- 6.4.3 Results 172 -- 6.4.3.1 UDP traffic 172 -- 6.4.3.2 TCP traffic 172 -- 6.5 Extending the Design to Support Fixed WLAN Users 174 -- 6.5.1 Network Architecture 174 -- 6.5.1.1 Uplink (UE→PDN) packet flow 176 -- 6.5.1.2 Downlink (PDN→UE) packet flow 177
  • 6.5.2 Handling Mobility 177 -- 6.6 Conclusion 178 -- References 179 -- 7 Improving the Effectiveness of Data Transfers in Mobile Computing Using Lossless Compression Utilities 181 Armen Dzhagaryan, Aleksandar Milenković and Martin Burtscher 7.1 Introduction 182 -- 7.2 Lossless Compression Utilities 185 -- 7.3 Experimental Setup 187 -- 7.3.1 Smartphone 187 -- 7.3.2 Measurement Setup 188 -- 7.3.3 Datasets 193 -- 7.4 Metrics and Experiments 194 -- 7.4.1 Metrics 194 -- 7.4.2 Experiments 195 -- 7.5 Results 197 -- 7.5.1 Compression Ratio 198 -- 7.5.2 Compression and Decompression Throughputs 198 -- 7.5.3 Energy Efficiency 204 -- 7.5.4 Putting It All Together 210 -- 7.6 RelatedWork 216 -- 7.7 Conclusions 217 -- References 219 PART III: Spectrum 8 Scheduling-Inspired Spectrum Assignment Algorithms for Mesh Elastic Optical Networks 225 Mahmoud Fayez, Iyad Katib, George N. Rouskas and Hossam M.
  • About the Editors 607
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