| Internet-Draft | Digital Twin Network Concept | March 2022 |
| Zhou, et al. | Expires 20 September 2022 | [Page] |
Digital Twin technology has been seen as a rapid adoption technology in Industry 4.0. The application of Digital Twin technology in the networking field is meant to develop various rich network applications and realize efficient and cost effective data driven network management and accelerate network innovation.¶
This document presents an overview of the concepts of Digital Twin Network, provides the basic definitions and a reference architecture, lists a set of application scenarios, and discusses the benefits and key challenges of such technology.¶
This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.¶
Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/.¶
Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress."¶
This Internet-Draft will expire on 20 September 2022.¶
Copyright (c) 2022 IETF Trust and the persons identified as the document authors. All rights reserved.¶
This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised BSD License.¶
The fast growth of network scale and the increased demand placed on these networks require them to accommodate and adapt dynamically to customer needs, implying a significant challenge to network operators. Indeed, network operation and maintenance are becoming more complex due to higher complexity of the managed networks and the sophisticated services they are delivering. As such, providing innovations on network technologies, management and operation will be more and more challenging due to the high risk of interfering with existing services and the higher trial costs if no reliable emulation platforms are available.¶
A Digital Twin is the real-time representation of a physical entity in the digital world. It has the characteristics of virtual-reality interrelation and real-time interaction, iterative operation and process optimization, full life-cycle and comprehensive data-driven network infrastructure. Currently, digital twin has been widely acknowledged in academic publications. See more in Section 3.¶
A digital twin for networks platform can be built by applying Digital Twin technologies to networks and creating a virtual image of physical network facilities (called herein, emulation). Basically, the digital twin for networks is an expansion platform of network simulation. The main difference compared to traditional network management systems is the interactive virtual-real mapping and data driven approach to build closed-loop network automation. Therefore, a digital twin network platform is more than an emulation platform or network simulator.¶
Through the real-time data interaction between the physical network and its twin network(s), the digital twin network platform might help the network designers to achieve more simplification, automatic, resilient, and full life-cycle operation and maintenance. More specifically, the digital twin network can, thus, be used to develop various rich network applications and assess specific behaviors (including network transformation) before actual implementation in the physical network, tweak the network for better optimized behavior, run 'what-if' scenarios that cannot be tested and evaluated easily in the physical network. In addition, service impact analysis tasks can also be facilitated.¶
This document makes use of the following terms:¶
The concept of the "twin" dates to the National Aeronautics and Space Administration (NASA) Apollo program in the 1970s, where a replica of space vehicles on Earth was built to mirror the condition of the equipment during the mission [Rosen2015].¶
In 2003, Digital Twin was attributed to John Vickers by Michael Grieves in his product lifecycle management (PLM) course as "virtual digital representation equivalent to physical products" [Grieves2014]. Digital twin can be defined as a virtual instance of a physical system (twin) that is continually updated with the latter's performance, maintenance, and health status data throughout the physical system's life cycle [Madni2019]. By providing a living copy of physical system, digital twins bring numerous advantages, such as accelerated business processes, enhanced productivity, and faster innovation with reduced costs. So far, digital twin has been successfully applied in the fields of intelligent manufacturing, smart city, or complex system operation and maintenance to help with not only object design and testing, but also management aspects [Tao2019].¶
Compared with 'digital model' and 'digital shadow', the key difference of 'digital twin' is the direction of data between the physical and virtual systems [Fuller2020]. Typically, when using a digital twin, the (twin) system is generated and then synchronized using data flows in both directions between physical and digital components, so that control data can be sent, and changes between the physical and digital objectives and systems are automatically represented. This behavior is unlike a 'digital model' or 'digital shadow', which are usually synchronized manually, lacking of control data, and might not have a full cycle of data integrated.¶
At present (2022), there is no unified definition of digital twin framework. The industry, scientific research institutions, and standards developing organizations are trying to define a general or domain-specific framework of digital twin. [Natis-Gartner2017] proposed that building a digital twin of a physical entity requires four key elements: model, data, monitoring, and uniqueness. [Tao2019] proposed a five-dimensional framework of digital twin {PE, VE, SS, DD, CN}, in which PE represents physical entity, VE represents virtual entity, SS represents service, DD represents twin data, and CN represents the connection between various components. [ISO-2021] issued a draft standard for digital twin manufacturing system, and proposed a reference framework including data collection domain, device control domain, digital twin domain, and user domain.¶
Communication networks can provide a solid foundation for implementing various 'digital twin' applications. At the same time, in the face of increasing business types, scale and complexity, a network itself also needs to use digital twin technology to seek better solutions beyond physical network. Since 2017, the application of digital twin technology in the field of communication networks has gradually been researched. Some examples are listed below.¶
In academy, [Dong2019] established the digital twin of 5G mobile edge computing (MEC) network, used the twin offline to train the resource allocation optimization and normalized energy-saving algorithm based on reinforcement learning, and then updated the scheme to MEC network. [Dai2020] established a digital twin edge network for mobile edge computing system, in which a twin edge server is used to evaluate the state of entity server, and the twin mobile edge computing system provides data for training offloading strategy. [Nguyen2021] discusses how to deploy a digital twin for complex 5G networks. [Hong2021] presents a digital twin platform towards automatic and intelligent management for data center networks, and then proposes a simplified the workflows of network service management. In addition, international workshops dedicated to digital twin in network field have already appeared, such as IEEE DTPI 2021 - Digital Twin Network Online Session [DTPI2021], or are being proposed such as IEEE NOMS 2022 - TNT workshop [TNT2022].¶
Although the application of digital twin technology in networking has started, the research of digital twin for networks technology is still in its infancy. Current applications focus on specific scenarios (such as network optimization), where network digital twin is just used as a network simulation tool to solve the problem of network operation and maintenance. Combined with the characteristics of digital twin technology and its application in other industries, this document believes that digital twin network can be regarded as an organic whole of the overall network system and become a general architecture involving the whole life cycle of physical network in the future, serving the application of network innovative technologies such as network planning, construction, maintenance and optimization, improving the automation and intelligence level of the network.¶
So far, there is no standard definition of "digital twin network" within the networking industry. This document defines "digital twin network" as a virtual representation of the physical network. Such virtual representation of the network is meant to be used to analyze, diagnose, emulate, and then control the physical network based on data, models, and interfaces. To that aim, a real-time and interactive mapping is required between the physical network and its virtual twin network.¶
Referring the characteristics of digital twin in other industries and the characteristics of the networking itself, the digital twin network should involve four key elements: data, mapping, models and interfaces as shown in Figure 1.¶
+-------------+ +--------------+
| | | |
| Mapping | | Interface |
| | | |
+-------------+-----------------+--------------+
| |
| Analyze, Diagnose |
| |
| +----------------------+ |
| | Digital Twin Network | |
| +----------------------+ |
+------------+ +------------+
| | Emulate, Control | |
| Models | | Data |
| |------------------------| |
+------------+ +------------+
A digital twin network should maintain historical data and/or real time data (configuration data, operational state data, topology data, trace data, metric data, process data, etc.) about its real-world twin (i.e. physical network) that are required by the models to represent and understand the states and behaviors of the real-world twin.¶
The data is characterized as the single source of "truth" and populated in the data repository, which provides timely and accurate data service support for building various models.¶
Techniques that involve collecting data from one or more sources in the real-world twin and developing a comprehensive representation of the data (e.g., system, entity, process) using specific models. These models are used as emulation and diagnosis basis to provide dynamics and elements on how the live physical network operates and generates reasoning data utilized for decision-making.¶
Various models such as service models, data models, dataset models, or knowledge graph can be used to represent the physical network assets and, then, instantiated to serve various network applications.¶
Standardized interfaces can ensure the interoperability of digital twin network. There are two major types of interfaces:¶
The former provides real-time data collection and control on the physical network. The latter helps in delivering application requests to the digital twin network platform and exposing the various platform capabilities to applications.¶
Used to identify the digital twin and the underlying entities and establish a real-time interactive relation between the physical network and the twin network or between two twin networks. The mapping can be:¶
Such mappings provide a good visibility of actual status, making the digital twin suitable to analyze and understand what is going on in the physical network. It also allows using the digital twin to optimize the performance and maintenance of the physical network.¶
The digital twin network constructed based on the four core technology elements can analyze, diagnose, emulate, and control the physical network in its whole life cycle with the help of optimization algorithms, management methods, and expert knowledge. One of the objectives of such control is to master the digital twin network environment and its elements to derive the required system behavior, e.g., provide:¶
Note: Real-time interaction is not always mandatory for all twins. When testing some configuration changes or trying some innovative techniques, the digital twins can behave as a simulation platform without the need of real time telemetry data. And even in this scenario, it is better to have interactive mapping capability so that the validated changes can be tested in real network whenever required by the testers. In most other cases (e.g., network optimization, network fault recovery), real-time interaction between virtual and real network is mandatory. This way, digital twin network can help achieve the goal of autonomous network or self-driven network.¶
Digital twin network can help enabling closed-loop network management across the entire lifecycle, from deployment and emulation, to visualized assessment, physical deployment, and continuous verification. By doing so, network operators and end-users to some extent, as allowed by specific application interfaces, can maintain a global, systemic, and consistent view of the network. Also, network operators and/or enterprise user can safely exercise the enforcement of network planning policies, deployment procedures, etc., without jeopardizing the daily operation of the physical network.¶
The main difference between digital twin network and simulation platform is the use of interactive virtual-real mapping to build closed-loop network automation. Simulation platforms are the predecessor of the digital twin network, one example of such a simulation platform is network simulator [NS-3], which can be seen as a variant of digital twin network but with low fidelity and lacking for interactive interfaces to the real network. Compared with those classical approaches, key benefits of digital twin network can be summarized as follows:¶
The following subsections further elaborate such benefits in details.¶
Large scale networks are complex to operate. Since there is no effective platform for simulation, network optimization designs have to be tested on the physical network at the cost of jeopardizing its daily operation and possibly degrading the quality of the services supported by the network. Such assessment greatly increases network operator's Operational Expenditure (OPEX) budgets too.¶
With a digital twin network platform, network operators can safely emulate candidate optimization solutions before deploying them in the physical network. In addition, operator's OPEX on the real physical network deployment will be greatly decreased accordingly at the cost of the complexity of the assessment and the resources involved.¶
Traditional network operation and management mainly focus on deploying and managing running services, but hardly support predictive maintenance techniques.¶
Digital twin network can combine data acquisition, big data processing, and AI modeling to assess the status of the network, but also to predict future trends, and better organize predictive maintenance. The ability to reproduce network behaviors under various conditions facilitates the corresponding assessment of the various evolution options as often as required.¶
Testing a new feature in an operational network is not only complex, but also extremely risky. Service impact analysis is required to be adequately achieved prior to effective activation of a new feature.¶
Digital twin network can greatly help assessing innovative network capabilities without jeopardizing the daily operation of the physical network. In addition, it helps researchers to explore network innovation (e.g., new network protocols, network AI/ML applications) efficiently, and network operators to deploy new technologies quickly with lower risks. Take AI/ ML application as example, it is a conflict between the continuous high reliability requirement (i.e., 99.999%) and the slow learning speed or phase-in learning steps of AI/ML algorithms. With digital twin network, AI/ML can complete the learning and training with the sufficient data before deploying the model in the real network. This would encourage more network AI innovations in future networks.¶
The requirements on data confidentiality and privacy on network providers increase the complexity of network management, as decisions made by computation logics such as an SDN controller may rely upon the packet payloads. As a result, the improvement of data-driven management requires complementary techniques that can provide a strict control based upon security mechanisms to guarantee data privacy protection and regulatory compliance. This may range from flow identification (using the archetypal five-tuple of addresses, ports and protocol) to techniques requiring some degree of payload inspection, all of them considered suitable to be associated to an individual person, and hence requiring strong protection and/or data anonymization mechanisms.¶
With strong modeling capability provided by the digital twin network, very limited real data (if at all) will be needed to achieve similar or even higher level of data-driven intelligent analysis. This way, a lower demand of sensitive data will permit to satisfy privacy requirements and simplify the use of privacy-preserving techniques for data-driven operation.¶
Network architectures can be complex, and their operation requires expert personnel. Digital twin network offers an opportunity to train staff for customized networks and specific user needs. Two salient examples are the application of new network architectures and protocols or the use of "cyber-ranges" to train security experts in threat detection and mitigation.¶
According to [Hu2021], the main challenges in building and mantaining digital twins can be summarized as the following five aspects:¶
Compared with other industrial fields, digital twin in networking field has its unique characteristics. On one hand, network elements and system have higher level of digitalization, which implies that data acquisition and virtual-real connection are relatively easy to achieve. On the other hand, there are many kinds of network elements and topologies in the network field; and the complex giant system of network carries a variety of business services. So, the construction of a digital twin network system needs to consdier the following major challenges:¶
In brief, to address the above listed challenges, it is important to firstly propose a unified architecture of digital twin network, which defines the main functional components and interfaces (Section 6). Then, relying upon such an architecture, it is required to continue researching on the key enabling technologies including data acquisition, data storage, data modeling, interface standardization, and security assurance.¶
Based on the definition of the key digital twin network technology elements introduced in Section 3.3, a digital twin network architecture is depicted in Figure 2. This digital twin network architecture is broken down into three layers: Application Layer, Digital Twin Layer, and Physical Network Layer.¶
+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure| Intent Input
| |
+-------------+-------------------v-----------------------+
| Instance of Digital Twin Network |
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Network| |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^----------------------------+-------------------+
| |
| data collection | control
+--------+----------------------------v-------------------+
| Physical Network |
| |
+---------------------------------------------------------+
All or subset of network elements in the physical network exchange network data and control messages with a network digital twin instance, through twin-physical control interfaces. The physical network can be a mobile access network, a transport network, a mobile core, a backbone, etc. The physical network can also be a data center network, a campus enterprise network, an industrial Internet of Things, etc.¶
The physical network can span across a single network administrative domain or multiple network administrative domains.¶
This document focuses on the IETF related physical network such as IP bearer network and datacenter network.¶
This layer includes three key subsystems: Data Repository subsystem, Service Mapping Models subsystem, and Digital Twin Network Management subsystem.¶
One or multiple digital twin network instances can be built and maintained:¶
Service Mapping Models complete data modeling, provide data model instances for various network applications, and maximizes the agility and programmability of network services. The data models include two major types: basic and functional models.¶
Functional models refer to various data models used for network analysis, emulation, diagnosis, prediction, assurance, etc. The functional models can be constructed and expanded by multiple dimensions: by network type, there can be models serving for a single or multiple network domains; by function type, it can be divided into state monitoring, traffic analysis, security exercise, fault diagnosis, quality assurance and other models; by network lifecycle management, it can be divided into planning, construction, maintenance, optimization and operation. Functional models can also be divided into general models and special-purpose models. Specifically, multiple dimensions can be combined to create a data model for more specific application scenarios.¶
New applications might need new functional models that do not exist yet. If a new model is needed, 'Service Mapping Models' subsystem will be triggered to help creating new models based on data retrieved from 'Data Repository'.¶
Notes: 'Data collection' and 'change control' are regarded as southbound interfaces between virtual and physical network. From implementation perspective, they can optionally form a sub-layer or sub-system to provide common functionalities of data collection and change control, enabled by a specific infrastructure supporting bi-directional flows and facilitating data aggregation, action translation, pre-processing and ontologies.¶
Implementing Intent-Based Networking (IBN) is an innovative technology for life-cycle network management. Future networks will be possibly Intent-based, which means that users can input their abstract 'intent' to the network, instead of detailed policies or configurations on the network devices. [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the concept of "Intent" and provides an overview of IBN functionalities. The key characteristic of an IBN system is that user intent can be assured automatically via continuously adjusting the policies and validating the real-time situation.¶
IBN can be envisaged in a digital twin network context to show how digital twin network improves the efficiency of deploying network innovation. To lower the impact on real networks, several rounds of adjustment and validation can be emulated on the digital twin network platform instead of directly on physical network. Therefore, digital twin network can be an important enabler platform to implement IBN systems and speed up their deployment.¶
Digital twin network can be applied to solve different problems in network management and operation.¶
The usual approach to network OAM with procedures applied by humans is open to errors in all these procedures, with impact in network availability and resilience. Response procedures and actions for most relevant operational requests and incidents are commonly defined to reduce errors to a minimum. The progressive automation of these procedures, such as predictive control or closed-loop management, reduce the faults and response time, but still there is the need of a human-in-the-loop for multiples actions. These processes are not intuitive and require training to learn how to respond.¶
The use of digital twin network for this purpose in different network management activities will improve the operators performance. One common example is cybersecurity incident handling, where "cyber-range" exercises are executed periodically to train security practitioners. Digital twin network will offer realistic environments, fitted to the real production networks.¶
Machine Learning requires data and their context to be available in order to apply it. A common approach in the network management environment has been to simulate or import data in a specific environment (the ML developer lab), where they are used to train the selected model, while later, when the model is deployed in production, re-train or adjust to the production environment context. This demands a specific adaption period.¶
Digital twin network simplifies the complete ML lifecycle development by providing a realistic environment, including network topologies, to generate the data required in a well-aligned context. Dataset generated belongs to the digital twin network and not to the production network, allowing information access by third parties, without impacting data privacy.¶
The potential application of CI/CD models network management operations increases the risk associated to deployment of non- validated updates, what conflicts with the goal of the certification requirements applied by network service providers. A solution for addressing these certification requirements is to verify the specific impacts of updates on service assurance and SLAs using a digital twin network environment replicating the network particularities, as a previous step to production release.¶
Digital twin network control functional block supports such dynamic mechanisms required by DevOps procedures.¶
Network management dependency on programmability increases systems complexity. The behavior of new protocol stacks, API parameters, and interactions among complex software components are examples that imply higher risk to errors or vulnerabilities in software and configuration.¶
Digital twin network allows to apply fuzzing testing techniques on a twin network environment, with interactions and conditions similar to the production network, permitting to identify and solve vulnerabilities, bugs and zero-days attacks before production delivery.¶
Research on digital twin network has just started. This document presents an overview of the digital twin network concepts and reference architecture. Looking forward, further elaboration on digital twin network scenarios, requirements, architecture, and key enabling technologies should be investigated by the industry, so as to accelerate the implementation and deployment of digital twin network.¶
This document describes concepts and definitions of digital twin network. As such, the following security considerations remain high level, i.e., in the form of principles, guidelines or requirements.¶
Security considerations of the digital twin network include:¶
Securing the digital twin network system aims at making the digital twin system operationally secure by implementing security mechanisms and applying security best practices. In the context of digital twin network, such mechanisms and practices may consist in data verification and model validation, mapping operations between physical network and digital counterpart network by authenticated and authorized users only.¶
Synchronizing the data between the physical and the digital twin networks may increase the risk of sensitive data and information leakage. Strict control and security mechanisms must be provided and enabled to prevent data leaks.¶
Many thanks to the NMRG participants for their comments and reviews. Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia, Jerome Francois, Jordi Paillisse, Luis Miguel Contreras Murillo, Alexander Clemm, Qiao Xiang, Ramin Sadre, Pedro Martinez-Julia, Wei Wang, Zongpeng Du, and Peng Liu.¶
Diego Lopez and Antonio Pastor were partly supported by the European Commission under Horizon 2020 grant agreement no. 833685 (SPIDER), and grant agreement no. 871808 (INSPIRE-5Gplus).¶
This document has no requests to IANA.¶
v06 - v07: Addressed reviewer's comments from adoption call, including below major changes.¶
v05 - v06: Addressed comments form meeting and maillist, to request adoptoin call.¶
04 - v05¶
v03 - v04¶
v02 - v03¶