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Checking references for intended status: Informational ---------------------------------------------------------------------------- == Outdated reference: A later version (-09) exists of draft-irtf-nmrg-ibn-concepts-definitions-01 Summary: 0 errors (**), 0 flaws (~~), 3 warnings (==), 1 comment (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Network Management Research Group C. Zhou 3 Internet-Draft H. Yang 4 Intended status: Informational X. Duan 5 Expires: January 13, 2021 China Mobile 6 July 12, 2020 8 Concepts of Digital Twin Network 9 draft-zhou-nmrg-digitaltwin-network-concepts-00 11 Abstract 13 Digital twin technology is becoming a hot technology in industry 4.0. 14 The application of digital twin technology in network field helps to 15 realize efficient and intelligent management and network innovation. 16 This document presents an overview of the concepts of Digital Twin 17 Network (DTN), provides the definition and DTN, and then describes 18 the benefits and key challenges of DTN. 20 Requirements Language 22 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 23 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 24 document are to be interpreted as described in RFC 2119 [RFC2119]. 26 Status of This Memo 28 This Internet-Draft is submitted in full conformance with the 29 provisions of BCP 78 and BCP 79. 31 Internet-Drafts are working documents of the Internet Engineering 32 Task Force (IETF). Note that other groups may also distribute 33 working documents as Internet-Drafts. The list of current Internet- 34 Drafts is at https://datatracker.ietf.org/drafts/current/. 36 Internet-Drafts are draft documents valid for a maximum of six months 37 and may be updated, replaced, or obsoleted by other documents at any 38 time. It is inappropriate to use Internet-Drafts as reference 39 material or to cite them other than as "work in progress." 41 This Internet-Draft will expire on January 13, 2021. 43 Copyright Notice 45 Copyright (c) 2020 IETF Trust and the persons identified as the 46 document authors. All rights reserved. 48 This document is subject to BCP 78 and the IETF Trust's Legal 49 Provisions Relating to IETF Documents 50 (https://trustee.ietf.org/license-info) in effect on the date of 51 publication of this document. Please review these documents 52 carefully, as they describe your rights and restrictions with respect 53 to this document. Code Components extracted from this document must 54 include Simplified BSD License text as described in Section 4.e of 55 the Trust Legal Provisions and are provided without warranty as 56 described in the Simplified BSD License. 58 Table of Contents 60 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 61 2. Definition of Digital Twin Network . . . . . . . . . . . . . 3 62 3. Benefits of Digital Twin Network . . . . . . . . . . . . . . 4 63 3.1. Lower the cost of network optimization . . . . . . . . . 4 64 3.2. More intelligent for network decision making . . . . . . 4 65 3.3. High efficient for network innovation . . . . . . . . . . 5 66 4. Challenges to build Digital Twin Network . . . . . . . . . . 5 67 5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 68 6. Security Considerations . . . . . . . . . . . . . . . . . . . 7 69 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7 70 8. References . . . . . . . . . . . . . . . . . . . . . . . . . 7 71 8.1. Normative References . . . . . . . . . . . . . . . . . . 7 72 8.2. Informative References . . . . . . . . . . . . . . . . . 7 73 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7 75 1. Introduction 77 With the advent of 5G, Internet of Things and Cloud Computing, the 78 scale of network is expanding constantly. Accordingly, the network 79 operation and maintenance are becoming more complex due to higher 80 complexity of network; and innovations on network will be more and 81 more difficult due to the higher risk of network failure and higher 82 trial cost. 84 Digital twin is the real-time representation of physical entities in 85 the digital world. It has the characteristics of virtual-reality 86 integration and real-time interaction, iterative operation and 87 optimization, as well as full life-cycle, and full business data- 88 driven. At present, it has been successfully applied in the fields 89 of intelligent manufacturing, smart city, complex system operation 90 and maintenance [Tao2019]. 92 A digital twin network platform can be built by applying digital twin 93 technology to network and creating virtual image of physical network 94 facilities. Through the real-time data interaction between physical 95 network and twin network, the digital twin network platform can help 96 the network to achieve more intelligent, efficient, safe and full 97 life-cycle operation and maintenance. 99 2. Definition of Digital Twin Network 101 So far, there is no standard definition of digital twin network in 102 networking industry or SDOs. This document attempts to define 103 Digitla Twin Network (DTN) as a virtual representation of the 104 physical network, analyzing, diagnosing, simulating and controlling 105 the physical network based on data, model and interface, so as to 106 achieve the real-time interactive mapping between physical network 107 and virtual twin network. According to the definition, DTN contains 108 four key elements: data, mapping, model and interface, as shown in 109 Figure 1. 111 +--------------+ 112 | | 113 | Interface | 114 | | 115 +-----+--------------+-----+ 116 | | 117 | Analyze, Diagnose | 118 +------------+ +------------+ 119 | | +----------------------+ | | 120 | Models | | NETWORK DIGITAL TWIN | | Data | 121 | | +----------------------+ | | 122 +------------+ +------------+ 123 | Simulate, Control | 124 | | 125 +-----+--------------+-----+ 126 | | 127 | Mappping | 128 | | 129 +--------------+ 131 Figure 1: Key Elements of Digital Twin Network 133 o Data is cornerstone for constructing a DTN system, in which 134 unified data repository can be the single source of the truth and 135 provide timely and accurate data support. 137 o Real-time interactive mapping between physical network and virtual 138 twin network is the most typical feature that DTN is different 139 from network simulation system. 141 o Data model is the ability source of DTN. Various data models can 142 be designed and flexibly combined to serve various network 143 applications. 145 o Standardized interface is the key technique enabler, which can 146 effectively ensure the compatibility and scalability of DTN 147 system. 149 3. Benefits of Digital Twin Network 151 DTN can help enable closed-loop network management across the entire 152 lifecycle, from digital deployment and simulation, to visualized 153 assessment, physical deployment, and continuous verification. In 154 doing so, customers are able to achieve network-wide insights, 155 precise planning, and rapid deployment in multiple areas, including 156 networks, services, users, and applications. All the benefits of DTN 157 can be categorized into three major types: low cost of network 158 optimization, intelligent network decision making, and high efficient 159 network innovation. The following sections describe the three types 160 of benefits respectively. 162 3.1. Lower the cost of network optimization 164 With extremely large scale, network is becoming more and more complex 165 and difficult to operate. Since there is no effective platform for 166 simulation, traditional network optimization has to be tried on real 167 network directly with long time cost and high service impact running 168 on real network. This also greatly increases network operator's 169 OpEX. 171 With DTN platform, network operators can well simulate the candidate 172 optimization solutions before finally deploy them to real network. 173 Compared with traditional methods, this is of quite low risk and will 174 bring much less impact on real network. In addition, the operator's 175 OpEX will be greatly decreased accordingly. 177 3.2. More intelligent for network decision making 179 Traditional network operation and management mainly focus on 180 deploying and managing current services, while lacking of handling 181 past data and predicting future status. This kind of passive and 182 protective maintenance is difficult to adapt to large-scale network 183 scenarios. 185 DTN can combine data acquisition, big data processing and AI modeling 186 to achieve the assessment of current status, diagnosis of past 187 problems, as well as prediction of future trends, then give the 188 results of analysis, simulate various possibilities, and provide more 189 comprehensive decision support. This will help network achieve 190 predictive maintenance from current protective maintenance. 192 3.3. High efficient for network innovation 194 Due to higher trial risk, real network environment is normally 195 unavailable to network researcher when they explore innovation 196 techniques. Instead, researchers have to use some offline simulation 197 platforms. This greatly impacts the real effectiveness of the 198 innovation, and greatly slow down the speed of network innovation. 199 Moreover, risk-averse network operators naturally reluctant to try 200 new technologies due to higher failure risk as well as the higher 201 failure cost. 203 DTN can generate virtual twin entity of the real network. This helps 204 researches explore network innovation (e.g. new network protocols, 205 network AI/ML applications, etc.) efficiently, and helps network 206 operators deploy new technologies quickly with lower risks. Take AI/ 207 ML application as example, it is a conflict between the continuous 208 high reliability requirement (i.e. 99.999%) of network and the slow 209 learning speed or phase-in learning steps of AI/ML algorithms. With 210 DTN platform, AI/ML can fully complete the leaning and training with 211 the sufficient data before deploy the model to the real network. 212 This will greatly encourage more network AI innovations in future 213 network. 215 Implementing Intent-Based Networking (IBN) via DTN can be another 216 example to show how DTN improves the efficiency of deploying network 217 innovation. IBN is an innovative technology for life-cycle network 218 management. Future network will be possibly Intent-based, which 219 means that users can input their abstract 'intent' to the network, 220 instead of detailed policies or configurations on the network 221 devices. [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the 222 concept of "Intent" and provides an overview of IBN functionalities. 223 The key character of an IBN system is that user's intent can be 224 assured automatically via continuously adjusting the policies and 225 validating the real-time situation. To lower the impact on real 226 network, several rounds of adjustment and validation can be simulated 227 on the DTN platform instead of directly on physical netowrk. 228 Therefore, DTN can be an important enabler platform to implement IBN 229 system and speed up the deployment of IBN in customer's network. 231 4. Challenges to build Digital Twin Network 233 As mentioned in above section, DTN can bring many benefits to network 234 management as well as network innovation. However, it is still 235 challenging to build an effective and efficient DTN system. The 236 following are the major challenges and problems. 238 o Large scale challenge: The digital twin entity of large-scale 239 network will significantly increase the complexity of data 240 acquisition and storage, the design and implementation of model. 241 And the requirements of software and hardware of the system will 242 be very high. 244 o Compatibility issue: It is difficult to establish a unified 245 digital twin platform with unified data model in the whole network 246 domain due to the inconsistency of technical implementation and 247 supporting functionalities of different manufacturers' devices in 248 the network. 250 o Data modeling difficulties: Based on large-scale network data, 251 data modeling should not only focus on ensuring the richness of 252 model functions, but also need to consider the flexibility and 253 scalability of the model. These requirements further increase the 254 difficulty of building efficient and hierarchical functional data 255 models. 257 o Real-time requirement: For services with high real-time 258 requirements, the processing of model simulation and verification 259 through DTN system will increase the service delay, so the 260 function and process of the data model need to increase the 261 processing mechanism under various network application scenarios; 262 at the same time, the real-time requirements will further increase 263 the system software and hardware performance requirements. 265 o Security risks: Network digital twin entity synchronizes all the 266 data of physical network in real time, which will increase the 267 security risk of user data, such as information leakage or more 268 vulnerable to attack. 270 To solve the above problems and challenges, Digital Twin Network 271 needs continuous optimization and breakthrough on key enabling 272 technologies including data acquisition, data storage, data modeling, 273 network visualization, interface standardization, and security 274 assurance, so as to meet the requirements of compatibility, 275 reliability, real-time and security under large-scale network. 277 5. Summary 279 The research and application of Digital Twin Network is just 280 beginning. This document presents an overview of the concepts and 281 definition of DTN. Looking forward, further researches on DTN usage 282 scenarios, requirements, architecture and key enabling technologies 283 should be promoted by the industry, so as to accelerate the 284 implementation and deployment of DTN in real network. 286 6. Security Considerations 288 TBD. 290 7. IANA Considerations 292 This document has no requests to IANA. 294 8. References 296 8.1. Normative References 298 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 299 Requirement Levels", BCP 14, RFC 2119, 300 DOI 10.17487/RFC2119, March 1997, 301 . 303 8.2. Informative References 305 [I-D.irtf-nmrg-ibn-concepts-definitions] 306 Clemm, A., Ciavaglia, L., Granville, L., and J. Tantsura, 307 "Intent-Based Networking - Concepts and Definitions", 308 draft-irtf-nmrg-ibn-concepts-definitions-01 (work in 309 progress), March 2020. 311 [Tao2019] Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in 312 Industry: State-of-the-Art. IEEE Transactions on 313 Industrial Informatics, vol. 15, no. 4.", April 2019. 315 Authors' Addresses 317 Cheng Zhou 318 China Mobile 319 Beijing 100053 320 China 322 Email: zhouchengyjy@chinamobile.com 324 Hongwei Yang 325 China Mobile 326 Beijing 100053 327 China 329 Email: yanghongwei@chinamobile.com 330 Xiaodong Duan 331 China Mobile 332 Beijing 100053 333 China 335 Email: duanxiaodong@chinamobile.com