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Pastor 8 Telefonica I+D 9 November 16, 2020 11 Concepts of Digital Twin Network 12 draft-zhou-nmrg-digitaltwin-network-concepts-02 14 Abstract 16 Digital twin technology is becoming a hot technology in industry 4.0. 17 The application of digital twin technology in network field helps to 18 realize efficient and intelligent management and network innovation. 19 This document presents an overview of the concepts of Digital Twin 20 Network (DTN), provides the definition and DTN, and then describes 21 the benefits and key challenges of DTN. 23 Requirements Language 25 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 26 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 27 document are to be interpreted as described in RFC 2119 [RFC2119]. 29 Status of This Memo 31 This Internet-Draft is submitted in full conformance with the 32 provisions of BCP 78 and BCP 79. 34 Internet-Drafts are working documents of the Internet Engineering 35 Task Force (IETF). Note that other groups may also distribute 36 working documents as Internet-Drafts. The list of current Internet- 37 Drafts is at https://datatracker.ietf.org/drafts/current/. 39 Internet-Drafts are draft documents valid for a maximum of six months 40 and may be updated, replaced, or obsoleted by other documents at any 41 time. It is inappropriate to use Internet-Drafts as reference 42 material or to cite them other than as "work in progress." 44 This Internet-Draft will expire on May 20, 2021. 46 Copyright Notice 48 Copyright (c) 2020 IETF Trust and the persons identified as the 49 document authors. All rights reserved. 51 This document is subject to BCP 78 and the IETF Trust's Legal 52 Provisions Relating to IETF Documents 53 (https://trustee.ietf.org/license-info) in effect on the date of 54 publication of this document. Please review these documents 55 carefully, as they describe your rights and restrictions with respect 56 to this document. Code Components extracted from this document must 57 include Simplified BSD License text as described in Section 4.e of 58 the Trust Legal Provisions and are provided without warranty as 59 described in the Simplified BSD License. 61 Table of Contents 63 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 64 2. Definition of Digital Twin Network . . . . . . . . . . . . . 3 65 3. Benefits of Digital Twin Network . . . . . . . . . . . . . . 4 66 3.1. Lower the cost of network optimization . . . . . . . . . 4 67 3.2. More intelligent for network decision making . . . . . . 5 68 3.3. High efficient for network innovation . . . . . . . . . . 5 69 3.4. Privacy and Regulatory Compliance . . . . . . . . . . . . 6 70 3.5. Customize Network Operation Training . . . . . . . . . . 6 71 4. Reference Architecture of Digital Twin Network . . . . . . . 6 72 5. Challenges to build Digital Twin Network . . . . . . . . . . 9 73 6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 74 7. Security Considerations . . . . . . . . . . . . . . . . . . . 10 75 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10 76 9. References . . . . . . . . . . . . . . . . . . . . . . . . . 10 77 9.1. Normative References . . . . . . . . . . . . . . . . . . 10 78 9.2. Informative References . . . . . . . . . . . . . . . . . 10 79 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10 81 1. Introduction 83 With the advent of 5G, Internet of Things and Cloud Computing, the 84 scale of network is expanding constantly. Accordingly, the network 85 operation and maintenance are becoming more complex due to higher 86 complexity of network; and innovations on network will be more and 87 more difficult due to the higher risk of network failure and higher 88 trial cost. 90 Digital twin is the real-time representation of physical entities in 91 the digital world. It has the characteristics of virtual-reality 92 integration and real-time interaction, iterative operation and 93 optimization, as well as full life-cycle, and full business data- 94 driven. At present, it has been successfully applied in the fields 95 of intelligent manufacturing, smart city, complex system operation 96 and maintenance [Tao2019]. 98 A digital twin network platform can be built by applying digital twin 99 technology to network and creating virtual image of physical network 100 facilities. Through the real-time data interaction between physical 101 network and twin network, the digital twin network platform can help 102 the network to achieve more intelligent, efficient, safe and full 103 life-cycle operation and maintenance. 105 2. Definition of Digital Twin Network 107 So far, there is no standard definition of digital twin network in 108 networking industry or SDOs. This document attempts to define 109 Digital Twin Network (DTN) as a virtual representation of the 110 physical network, analyzing, diagnosing, simulating and controlling 111 the physical network based on data, model and interface, so as to 112 achieve the real-time interactive mapping between physical network 113 and virtual twin network. According to the definition, DTN contains 114 five key elements: data, mapping, model, interface and orchestration 115 stack, as shown in Figure 1. 117 +------------+ 118 | | 119 | Interface | 120 +------------+ | | +------------+ 121 | |------+------------+------| | 122 | Models | | Data | 123 | | Analyze, Diagnose | | 124 +------------+ +------------+ 125 | +----------------------+ | 126 | | NETWORK DIGITAL TWIN | | 127 | +----------------------+ | 128 | | 129 | Simulate, Control | 130 | | 131 +-------------+--------------+--------------+ 132 | | | | 133 | Mapping | |Orchestration | 134 | | | | 135 +-------------+ +--------------+ 137 Figure 1: Key Elements of Digital Twin Network 139 o Data is cornerstone for constructing a DTN system, in which 140 unified data repository can be the single source of the truth and 141 provide timely and accurate data support. 143 o Real-time interactive mapping between physical network and virtual 144 twin network is the most typical feature that DTN is different 145 from network simulation system. 147 o Data model is the ability source of DTN. Various data models can 148 be designed and flexibly combined to serve various network 149 applications. 151 o Standardized interface is the key technique enabler, which can 152 effectively ensure the compatibility and scalability of DTN 153 system. 155 o The orchestration stack controls the flows of data and control 156 actions. It relies on the dynamic lifecycle management of network 157 models and elements to provide repeatablity (the capacity to 158 replicate network conditions on demand) and reproducibility (the 159 ability to replay successions of events, possibly under controlled 160 variations). 162 3. Benefits of Digital Twin Network 164 DTN can help enable closed-loop network management across the entire 165 lifecycle, from digital deployment and simulation, to visualized 166 assessment, physical deployment, and continuous verification. In 167 doing so, customers are able to achieve network-wide insights, 168 precise planning, and rapid deployment in multiple areas, including 169 networks, services, users, and applications. All the benefits of DTN 170 can be categorized into three major types: low cost of network 171 optimization, intelligent network decision making, and high efficient 172 network innovation. The following sections describe the three types 173 of benefits respectively. 175 3.1. Lower the cost of network optimization 177 With extremely large scale, network is becoming more and more complex 178 and difficult to operate. Since there is no effective platform for 179 simulation, traditional network optimization has to be tried on real 180 network directly with long time cost and high service impact running 181 on real network. This also greatly increases network operator's 182 OpEX. 184 With DTN platform, network operators can well simulate the candidate 185 optimization solutions before finally deploy them to real network. 186 Compared with traditional methods, this is of quite low risk and will 187 bring much less impact on real network. In addition, the operator's 188 OpEX will be greatly decreased accordingly. 190 3.2. More intelligent for network decision making 192 Traditional network operation and management mainly focus on 193 deploying and managing current services, while lacking of handling 194 past data and predicting future status. This kind of passive and 195 protective maintenance is difficult to adapt to large-scale network 196 scenarios. 198 DTN can combine data acquisition, big data processing and AI modeling 199 to achieve the assessment of current status, diagnosis of past 200 problems, as well as prediction of future trends, then give the 201 results of analysis, simulate various possibilities, and provide more 202 comprehensive decision support. This will help network achieve 203 predictive maintenance from current protective maintenance. The 204 network behavioral repeatability and reproducibility properties in 205 the DTN allow to evaluate different conditions and controlled 206 variations of them, exploring choice as many times as needed to apply 207 the better emulation and decision procedures. 209 3.3. High efficient for network innovation 211 Due to higher trial risk, real network environment is normally 212 unavailable to network researcher when they explore innovation 213 techniques. Instead, researchers have to use some offline simulation 214 platforms. This greatly impacts the real effectiveness of the 215 innovation, and greatly slow down the speed of network innovation. 216 Moreover, risk-averse network operators naturally reluctant to try 217 new technologies due to higher failure risk as well as the higher 218 failure cost. 220 DTN can generate virtual twin entity of the real network. This helps 221 researches explore network innovation (e.g. new network protocols, 222 network AI/ML applications, etc.) efficiently, and helps network 223 operators deploy new technologies quickly with lower risks. Take AI/ 224 ML application as example, it is a conflict between the continuous 225 high reliability requirement (i.e. 99.999%) of network and the slow 226 learning speed or phase-in learning steps of AI/ML algorithms. With 227 DTN platform, AI/ML can fully complete the leaning and training with 228 the sufficient data before deploy the model to the real network. 229 This will greatly encourage more network AI innovations in future 230 network. 232 Implementing Intent-Based Networking (IBN) via DTN can be another 233 example to show how DTN improves the efficiency of deploying network 234 innovation. IBN is an innovative technology for life-cycle network 235 management. Future network will be possibly Intent-based, which 236 means that users can input their abstract 'intent' to the network, 237 instead of detailed policies or configurations on the network 238 devices. [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the 239 concept of "Intent" and provides an overview of IBN functionalities. 240 The key character of an IBN system is that user's intent can be 241 assured automatically via continuously adjusting the policies and 242 validating the real-time situation. To lower the impact on real 243 network, several rounds of adjustment and validation can be simulated 244 on the DTN platform instead of directly on physical netowrk. 245 Therefore, DTN can be an important enabler platform to implement IBN 246 system and speed up the deployment of IBN in customer's network. 248 3.4. Privacy and Regulatory Compliance 250 The requirements on data confidentiality and privacy on network 251 service providers increase the complexity of network management, as 252 intelligent decision engines depend on data flows. As a result, the 253 improvement of data-enabled management requires complementary 254 techniques providing strict control and security mechanisms to 255 guarantee data privacy protection and regulatory compliance in these 256 aspects. Some examples of these techniques can include payload 257 inspection, including de-encryption user explicit consents, or data 258 anonymization mechanisms. 260 Given DTN works with mapped traffic or services from real networks, 261 but using traffic simulations, including automated tools for 262 synthetic user activity. The lack of personal data permits to lower 263 the privacy requirements and simplify privacy-preserving techniques, 264 as the data is not coming from real users. As a result, DTN allows 265 to focus on management improvements, without other concerns. 266 Additionally, logging and auditing the DTN experiments and synthetic 267 user activities provide additional information for further design and 268 planning, without the need of traffic inspection. 270 3.5. Customize Network Operation Training 272 Networks architectures can be complex, and their operation and 273 management require expert personnel and the learning curve can be 274 steep in most cases. DTN offers an opportunity to train staff for 275 customized networks and specific user needs. Several areas can 276 benefit with the use of it. Two salient examples are the application 277 of new network architectures and protocols, or the use of cyber- 278 ranges to train security experts in threat detection and mitigation. 280 4. Reference Architecture of Digital Twin Network 282 So far, there is no reference or standard architecture for Digital 283 Twin Network in network domain. Based on the definition of key 284 elements of DTN described in section 2, reference architecture with 285 three layers of Digital Twin Network can be designed as below, shown 286 in Figure 2. 288 +---------------------------------------------------------+ 289 | +-------+ +-------+ +-------+ Network| 290 | | App 1 | | App 2 | ... | App n | Application| 291 | +-------+ +-------+ +-------+ | 292 +-------------^-------------------+-----------------------+ 293 | ability supply |intent input 294 | | 295 +---------------------------------v-----------------------+ 296 | Network Digital Twin| 297 | +--------+ +------------------------+ +--------+ | 298 | | | | Service Mapping Models | | | | 299 | | | | +------------------+ | | | | 300 | | Data +---> |Functional Models | +---> Digital| | 301 | | Sharing| | +-----+-----^------+ | | Twin | | 302 | | Repo- | | | | | | Entity | | 303 | | sitory | | +-----v-----+------+ | | Mngmt | | 304 | | <---+ | Basic Models | <---+ | | 305 | | | | +------------------+ | | | | 306 | +--------+ +------------------------+ +--------+ | 307 +--------^------------------------------------------------+ 308 | | 309 | data collection | control 310 +-------------------------------------v-------------------+ 311 | Physical Network| 312 | Network infrastructures | 313 +---------------------------------------------------------+ 315 Figure 2: Reference Architecutre of Digital Twin Network 317 1. Bottom layer is Physical Network. All network elements in 318 physical network exchange massive network data and control with 319 network digital twin entity, via southbound interfaces. Physical 320 network can be either telecommunication operator network, or data 321 center network, campus network, industrial Internet of things or 322 other network types. 324 2. Middle layer is Network Digital Twin Entity, which is the core of 325 DTN system. This layer includes three key subsystems: Data 326 Sharing Repository, Service Mapping Models and Digital Twin 327 Entity Management. 329 * Data Sharing Repository provides accurate and complete 330 information for building various service models by collecting 331 and updating the real-time operational data of various network 332 elements through the southbound interface. In addition to 333 data storage, Data Sharing Repository is also responsible to 334 provide data services for the Service Mapping Models sub- 335 system, including fast retrieval, concurrent conflict, batch 336 service, unified interface, etc. 338 * Service Mapping Models completes data-based modelling, 339 provides data model instances for various network 340 applications, and maximizes the agility and programmability of 341 network services. The data models include two major types: 342 basic models and functional models. 344 + Basic Model refers to the network element model and network 345 topology model of the network digital twin entity based on 346 the basic configuration, environment information, 347 operational state, link topology and other information of 348 the network element, to complete the real-time accurate 349 description of the physical network. 351 + Functional model refers to various data models such as 352 network analysis, simulation, diagnosis, prediction, 353 assurance, etc. The functional models can be constructed 354 and expanded by multiple dimensions: by network type, there 355 can be models serving for single network domain or multi 356 network domain; by function type, it can be divided into 357 state monitoring, traffic analysis, security drill, fault 358 diagnosis, quality assurance and other models; by 359 generality, it can be divided into general model and 360 special-purpose model. Specifically, multiple dimensions 361 can be combined to create a data model for more specific 362 application scenario. 364 * Digital Twin Entity Management completes the management 365 function of digital twin network, records the life-cycle of 366 the entity, visualizes and controls various elements of 367 network digital twin, including topology management, model 368 management and security management. 370 3. Top layer is Network Application. Various applications (e.g. 371 Network intelligent O&M, IBN, etc.) can effectively run against 372 Digital Twin Network platform to implement either conventional or 373 innovative network operations, with low cost and less service 374 impact on real network. Network application provide requirements 375 to network digital twin entity via northbound interface; then the 376 service is simulated by various service model instances; after 377 fully verified, the change control can be deployed safely to 378 physical network. 380 5. Challenges to build Digital Twin Network 382 As mentioned in above section, DTN can bring many benefits to network 383 management as well as network innovation. However, it is still 384 challenging to build an effective and efficient DTN system. The 385 following are the major challenges and problems. 387 o Large scale challenge: The digital twin entity of large-scale 388 network will significantly increase the complexity of data 389 acquisition and storage, the design and implementation of model. 390 And the requirements of software and hardware of the system will 391 be very high. 393 o Compatibility issue: It is difficult to establish a unified 394 digital twin platform with unified data model in the whole network 395 domain due to the inconsistency of technical implementation and 396 supporting functionalities of different manufacturers' devices in 397 the network. 399 o Data modeling difficulties: Based on large-scale network data, 400 data modeling should not only focus on ensuring the richness of 401 model functions, but also need to consider the flexibility and 402 scalability of the model. These requirements further increase the 403 difficulty of building efficient and hierarchical functional data 404 models. 406 o Real-time requirement: For services with high real-time 407 requirements, the processing of model simulation and verification 408 through DTN system will increase the service delay, so the 409 function and process of the data model need to increase the 410 processing mechanism under various network application scenarios; 411 at the same time, the real-time requirements will further increase 412 the system software and hardware performance requirements. 414 o Security risks: Network digital twin entity synchronizes all the 415 data of physical network in real time, which will increase the 416 security risk of user data, such as information leakage or more 417 vulnerable to attack. 419 To solve the above problems and challenges, Digital Twin Network 420 needs continuous optimization and breakthrough on key enabling 421 technologies including data acquisition, data storage, data modeling, 422 network visualization, interface standardization, and security 423 assurance, so as to meet the requirements of compatibility, 424 reliability, real-time and security under large-scale network. 426 6. Summary 428 The research and application of Digital Twin Network is just 429 beginning. This document presents an overview of the concepts and 430 definition of DTN. Looking forward, further researches on DTN usage 431 scenarios, requirements, architecture and key enabling technologies 432 should be promoted by the industry, so as to accelerate the 433 implementation and deployment of DTN in real network. 435 7. Security Considerations 437 TBD. 439 8. IANA Considerations 441 This document has no requests to IANA. 443 9. References 445 9.1. Normative References 447 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 448 Requirement Levels", BCP 14, RFC 2119, 449 DOI 10.17487/RFC2119, March 1997, 450 . 452 9.2. Informative References 454 [I-D.irtf-nmrg-ibn-concepts-definitions] 455 Clemm, A., Ciavaglia, L., Granville, L., and J. Tantsura, 456 "Intent-Based Networking - Concepts and Definitions", 457 draft-irtf-nmrg-ibn-concepts-definitions-02 (work in 458 progress), September 2020. 460 [Tao2019] Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in 461 Industry: State-of-the-Art. IEEE Transactions on 462 Industrial Informatics, vol. 15, no. 4.", April 2019. 464 Authors' Addresses 466 Cheng Zhou 467 China Mobile 468 Beijing 100053 469 China 471 Email: zhouchengyjy@chinamobile.com 472 Hongwei Yang 473 China Mobile 474 Beijing 100053 475 China 477 Email: yanghongwei@chinamobile.com 479 Xiaodong Duan 480 China Mobile 481 Beijing 100053 482 China 484 Email: duanxiaodong@chinamobile.com 486 Diego Lopez 487 Telefonica I+D 488 Seville 489 Spain 491 Email: diego.r.lopez@telefonica.com 493 Antonio Pastor 494 Telefonica I+D 495 Madrid 496 Spain 498 Email: antonio.pastorperales@telefonica.com