Provisioning, Recovery, and In-Operation Planning in Elastic Optical Networks
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Content
List of Contributors xiii
1 Motivation 1
1.1 Motivation 1
1.2 Book Outline 8
1.3 Book Itineraries 11
Acknowledgment 12
Part I Introduction 13
2 Background 15
2.1 Introduction to Graph Theory 16
2.2 Introduction to Optimization 20
2.3 ILP Models and Heuristics for Routing Problems 22
2.3.1 ILP Formulations 22
2.3.2 Heuristics 25
2.3.3 Meta?]Heuristics 27
2.4 Introduction to the Optical Technology 30
2.4.1 From Opaque to Transparent Optical Networks 31
2.4.2 Single?]Layer and Multilayer Networks 32
2.4.3 EON Key Technologies 33
2.5 Network Life Cycle 35
2.5.1 Connection Provisioning 36
2.5.2 Connection Recovery 37
2.6 Conclusions 40
3 The Routing and Spectrum Allocation Problem 43
3.1 Introduction 44
3.2 The RSA Problem 45
3.2.1 Basic Offline Problem Statement 45
3.2.2 Notation 46
3.3 ILP Formulations Based On Slice Assignment 47
3.3.1 Starting Slice Assignment RSA (SSA?]RSA) Formulation 47
3.3.2 Slice Assignment RSA (SA?]RSA) Formulation 48
3.4 ILP Formulations Based On Slot Assignment 49
3.4.1 Slot Precomputation 49
3.4.2 Slot Assignment RSA (CA?]RSA) Formulation 50
3.5 Evaluation of the ILP Formulations 51
3.5.1 Model Size Analysis 51
3.5.2 Performance Comparison 52
3.5.3 Evaluation in Real Scenarios 54
3.6 The RMSA Problem 56
3.6.1 Notation Extensions 56
3.6.2 Basic Offline Problem 56
3.6.3 Topology Design Problem as an RMSA Problem 57
3.7 Conclusions 60
4 Architectures for Provisioning and In?]operation Planning 61
4.1 Introduction 62
4.2 Architectures for Dynamic Network Operation 64
4.2.1 Static versus Dynamic Network Operation 64
4.2.2 Migration toward In?]operation Network Planning 65
4.2.3 Required Functionalities 67
4.2.4 The Front?]end/Back?]end PCE Architecture 68
4.3 In?]operation Planning: Use Cases 73
4.3.1 VNT Reconfiguration after a Failure 73
4.3.2 Reoptimization 76
4.4 Toward Cloud?]Ready Transport Networks 78
4.5 Conclusions 84
Part II Provisioning in Single Layer Networks 85
5 Dynamic Provisioning of p2p Demands 87
5.1 Introduction 88
5.2 Provisioning in Transparent Networks 90
5.2.1 Problem Statement 90
5.2.2 Dynamic RSA Algorithm 90
5.2.3 Dynamic RMSA Algorithm 91
5.2.4 Bulk RSA Algorithm 92
5.2.5 Illustrative Results 93
5.3 Provisioning in Translucent Networks 99
5.4 Dynamic Spectrum Allocation Adaption 102
5.4.1 Spectrum Allocation Policies 103
5.4.2 Problem Statement 104
5.4.3 Spectrum Adaption Algorithms 105
5.4.4 Illustrative Results 106
5.5 Conclusions 110
6 Transfer?]based Datacenter Interconnection 113
6.1 Introduction 114
6.2 Application Service Orchestrator 116
6.2.1 Models for Transfer?]based Connections 117
6.2.2 Illustrative Results 121
6.3 Routing and Scheduled Spectrum Allocation 124
6.3.1 Managing Transfer?]based Connections 124
6.3.2 The RSSA Problem 126
6.3.3 ILP Formulation 127
6.3.4 Algorithms to Manage Transfer?]based Requests 130
6.3.5 Illustrative Results 132
6.4 Conclusions 138
7 Provisioning Multicast and Anycast Demands 141
7.1 Introduction 142
7.2 Multicast Provisioning 143
7.2.1 P2MP?]RSA Problem Statement 145
7.2.2 ILP Formulation 145
7.2.3 Heuristic Algorithm 148
7.2.4 Illustrative Numerical Results 150
7.2.5 Proposed Workflows and Protocol Issues 152
7.2.6 Experimental Assessment 154
7.3 Anycast Provisioning 156
7.3.1 Optical Anycast (AC_RSA) Problem Statement 157
7.3.2 Exact Algorithm for the AC_RSA Problem 157
7.3.3 Illustrative Numerical Results 158
7.3.4 Proposed Workflow 159
7.3.5 Experimental Assessment 161
7.4 Conclusions 162
Part III Recovery and In?]operation Planning in Single Layer Networks 163
8 Spectrum Defragmentation 165
8.1 Introduction 166
8.2 Spectrum Reallocation and Spectrum Shifting 168
8.3 Spectrum Reallocation: The SPRESSO Problem 170
8.3.1 Problem Statement 170
8.3.2 ILP Formulation 170
8.3.3 Heuristic Algorithm 172
8.4 Spectrum Shifting: The SPRING Problem 178
8.4.1 Problem Statement 178
8.4.2 ILP Formulation 178
8.4.3 Heuristic Algorithm 179
8.5 Performance Evaluation 180
8.5.1 SPRESSO Heuristics Tuning 180
8.5.2 Heuristics versus the ILP Model 182
8.5.3 Performance of the SPRESSO Algorithm 182
8.6 Experimental Assessment 184
8.6.1 Proposed Workflow and Algorithm 184
8.6.2 PCEP Issues 186
8.6.3 Experiments 188
8.7 Conclusions 191
9 Restoration in the Optical Layer 193
9.1 Introduction 194
9.2 Bitrate Squeezing and Multipath Restoration 195
9.2.1 The BATIDO Problem 197
9.2.2 ILP Formulation 197
9.2.3 Heuristic Algorithm 200
9.2.4 Numerical Results 202
9.3 Modulation Format?]Aware Restoration 207
9.3.1 The MF?]Restoration Problem 210
9.3.2 Algorithm for MF?]Restoration 211
9.3.3 Protocol Extensions and Proposed Workflows 213
9.3.4 Experimental Assessment 216
9.4 Recovering Anycast Connections 216
9.4.1 ILP Formulations and Algorithm 217
9.4.2 Proposed Workflow 220
9.4.3 Validation 221
9.5 Conclusions 223
10 After?]Failure?]Repair Optimization 225
10.1 Introduction 226
10.2 The AFRO Problem 228
10.2.1 Problem Statement 230
10.2.2 Optimization Algorithm 230
10.2.3 ILP Formulation 231
10.2.4 Heuristic Algorithm 233
10.2.5 Disruption Considerations 234
10.2.6 Performance Evaluation 236
10.3 Restoration and AFRO with Multiple Paths 240
10.3.1 Problem Statement 242
10.3.2 MILP Formulation 242
10.3.3 Heuristic Algorithm 244
10.3.4 MP?]AFRO Performance Evaluation 245
10.4 Experimental Validation 246
10.4.1 Proposed Reoptimization Workflow 246
10.4.2 Experimental Assessment 249
10.5 Conclusions 252
Part IV Multilayer Networks 255
11 Virtual Network Topology Design and Reconfiguration 257
11.1 Introduction 258
11.2 VNT Design and Reconfiguration Options 259
11.3 Static VNT Design 262
11.3.1 The VNT Design Problem 262
11.3.2 MILP Formulation 262
11.4 VNT Reconfiguration Based on Traffic Measures 264
11.4.1 The VENTURE Problem 264
11.4.2 ILP Formulation 265
11.4.3 Heuristic Algorithm 267
11.4.4 Proposed Workflow 272
11.5 Results 273
11.5.1 Simulation Results 273
11.5.2 Experimental Assessment 275
11.6 Conclusions 278
12 Recovery in Multilayer Networks 279
12.1 Introduction 280
12.2 Path Restoration in GMPLS?]Controlled Networks 281
12.2.1 The DYNAMO Problem 285
12.2.2 MP Formulation 285
12.2.3 Heuristic Algorithm 290
12.2.4 DYNAMO Numerical Results 290
12.2.5 PCE Architecture 297
12.2.6 Experimental Results 299
12.3 Survivable VNT for DC Synchronization 302
12.3.1 Mathematical Formulations and Algorithms 304
12.3.2 Workflows and Protocol Extensions 309
12.3.3 Experimental Assessment 310
12.4 Conclusions 312
Part V Future Trends 313
13 High Capacity Optical Networks Based on Space Division Multiplexing 315
13.1 Introduction 316
13.2 SDM Fibers 319
13.2.1 Uncoupled/Weakly Coupled Spatial Dimensions 320
13.2.2 Strongly Coupled Spatial Dimensions 320
13.2.3 Subgroups of Strongly Coupled Spatial Dimensions 321
13.3 SDM Switching Paradigms 322
13.4 Resource Allocation in SDM Networks 325
13.5 Impact of Traffic Profile on the Performance of Spatial Sp?]Ch Switching in SDM Networks 332
13.5.1 Illustrative Results 333
13.6 Impact of Spatial and Spectral Granularity on the Performance of SDM Networks Based on Spatial Sp?]Ch Switching 336
13.6.1 Illustrative Results 338
13.7 Conclusions 342
14 Dynamic Connectivity Services in Support of Future Mobile Networks 345
14.1 Introduction 346
14.2 C?]RAN Requirements and CVN Support 348
14.2.1 C?]RAN Architecture Model 349
14.2.2 Backhaul Requirements in C?]RAN 349
14.2.3 CVN Reconfiguration 351
14.3 The CUVINET Problem 354
14.3.1 Problem Statement 354
14.3.2 MILP Formulation 355
14.3.3 Heuristic Algorithm 359
14.4 Illustrative Numerical Results 361
14.4.1 Network Scenario 361
14.4.2 Heuristic Algorithm Validation 362
14.4.3 Approaches to Support CVNs 362
14.4.4 Performance Evaluation 363
14.5 Conclusions 367
15 Toward Cognitive In?]operation Planning 369
15.1 Introduction 370
15.2 Data Analytics for Failure Localization 371
15.2.1 Algorithm for Failure Identification/Localization 372
15.2.2 Experiments and Results 375
15.2.3 Generic Modules to Implement the OAA Loop 377
15.3 Data Analytics to Model Origin-Destination Traffic 378
15.3.1 Generic Modules for VNT Reconfiguration Based on Traffic Modeling 378
15.3.2 Machine Learning Procedure for Traffic Estimation 380
15.3.3 Use Case I: Anomaly Detection 383
15.3.4 Use Case II: VNT Reconfiguration Triggered by Anomaly Detection 390
15.4 Adding Cognition to the ABNO Architecture 393
15.5 Conclusions 395
List of Acronyms 397
References 403
Index 419
