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HomeUncategorizedHow digital twins are changing network infrastructure: The future state (Part 2)

How digital twins are changing network infrastructure: The future state (Part 2)

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This is the second part of a two-part series. ReadPart 1

on the current state of the web and how digital twins can be used to help automate processes and the drawbacks involved.


As described in Part 1, digital twins are beginning to automate the process of bringing digital transformation to the network play a key role in the process of infrastructure. Today, we explore the future state of digital twins – comparing how they are used now with how they will be used once the technology matures.

The digital twins market is expected to grow at a CAGR (CAGR) of up to 35% between 2022 and 2027, increasing its valuation from $10.3 billion to $61.5 billion Dollar. Internet of Things (IoT) devices are driving a large part of this growth, and campus networks represent a key aspect of the infrastructure needed to support the widespread deployment of more and more IoT devices.

Current limitations of digital twins

One of the problems plaguing the use of digital twins today is that network digital twins often only facilitate building parts of the network that are segregated by function, vendor, or user type. mold and automation. However, enterprise demands for more flexible and agile network infrastructure are driving efforts to consolidate these pockets.

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    Multiple networking vendors such as Forward Networks, Gluware, Intentionet and Keysight’s recent acquisition of Scalable Networks began Enable digital twins that work across vendors to improve configuration management, security, compliance, and performance.

    Companies like Asperitas and Villa Tech are creating “digital twins as a service” to help businesses run.

    In addition to the challenges of building a digital twin for a multi-vendor network, digital twin technology needs to overcome other limitations before it can be fully adopted, including:

    The model type used in the digital twin needs to match the actual use case.

      According to Balaji Venkatraman, vice president of product management for Cisco DNA, building models, supporting multiple models, and evolving models over time all require significant investment.

  • Save the state of data lake streams and networks. If the digital twin operates on old data, it will return outdated answers.

  • Future solutions

    Manas Tiwari, customer partner for Capgemini Engineering’s cross-industry communication solutions, believes that digital twins will help in the launch of The classified networks, topologies and service providers of different devices are done in the same way that enterprises now provide services across multiple cloud services.

    Digital twins will make it easier to pre-model different network designs and then fine-tune them to ensure they work as expected, Tiwari said. This is critical for the widespread rollout of healthcare, factories, warehouses, and new IoT businesses.

    Vendors such as Gluware, Forward Networks and others are creating real-time digital twins to simulate network, security and automation environments in order to predict possible problems before these environments are rolled out. These tools are also starting to plug into continuous integration and continuous deployment (CI/CD) tools to support incremental updates and rollbacks using existing devops processes. if analysis, change impact analysis, network sizing and capacity planning. These areas are critical for proactive and predictive analytics to prevent network or service downtime or adversely impact user experience.

    Overcome with new agreement The struggle

    Early modeling Simulation tools such as GNS3 Virtual Lab help network engineers understand what is happening in the network What happens, including traffic paths, connectivity and isolation of network elements. Still, they often struggle with new protocols, domains, or expanding to the wider network. They also need to simulate the ideal flow of traffic, and all the ways it could break, or the path could be isolated from the rest of the network.

    One of the biggest challenges is that real network traffic is random, Christopher Grammer, vice president of solutions technology at IT solutions provider Calian, told VentureBeat. The web traffic generated by a coffee shop full of casual internet users is a far cry from the needs of petroleum engineers working on real-time drilling operations. Therefore, the simulated network performance is subject to user demands, changes at any time, and is more difficult to proactively predict.

    Not only that, but the modeling tools are also expensive to scale up.

    “The cost difference between simulating a relatively simple residential network model and the AT&T Internet backbone is astronomical,” Grammer said.

    Due to improvements in algorithms and hardware, vendors such as Forward Enterprise are beginning to scale these computations to support networks consisting of hundreds of thousands of devices.

    Test new configuration

      The highest use case for a connected digital twin is evaluating different configuration settings before updating or installing a new device. Digital twins can help assess the likely impact of changes to ensure equipment works as intended.

      In theory, these end up making it easier to assess the performance impact of changes. However, Mike Toussaint, a senior director analyst at Gartner, said it may take some time to develop new modeling and simulation tools to account for the performance of the new chips.

      The exciting aspect is that these modeling and simulation capabilities are now being integrated with IT automation. This allows engineers to connect in-circuit testing and simulation with the tools used to build, configure, develop and deploy networks, said Ernest Lefner, chief product officer at Gluware, which enables intelligent network process automation.

      “You can now understand malfunctions, errors and broken functions before you push a button and cause an outage. Combining these key functions with automation builds confidence that you’re doing Changes will be right the first time,” he said.

      Wireless Analytics

    Equipment vendors such as Juniper are using artificial intelligence (AI) to integrate Telemetry and analytics to automatically capture information about wireless infrastructure to determine the optimal placement of wireless networks. Ericsson has started using Nvidia Omniverse to simulate 5G reception in cities. Nearmap recently partnered with Digital Twin Sims to create dynamically updated 5G coverage maps into 5G planning and operating systems.

    Security and Compliance

    Grammer says digital twins can help improve network heuristics and behaviors for network security management Analytical aspects. This can help identify potentially harmful or malicious traffic, such as botnets or ransomware. Security companies often model known good and bad network traffic to teach machine learning algorithms to identify suspicious network traffic.

    According to Lefner, digital twins can model real-time data streams for complex auditing and security compliance tasks.

    “It’s exciting to consider taking a complex annual audit task for something like PCI compliance and reducing it to an automated task that can be reviewed on a daily basis ,” he said.

    Combining these digital twins with automation enables one-step change in challenging tasks such as identifying the latest software and fixing newly discovered vulnerabilities. For example, Gluware combines modeling, simulation and robotic process automation (RPA) to allow software robots to take actions based on specific network conditions.

    Forward Networks co-founder Peyman Kazemian said they started using digital twins to model network infrastructure. When a new vulnerability is discovered in a specific type of device or software version, the digital twin can find all hosts reachable from less trusted entry points to prioritize remediation efforts.

    Cross-domain collaboration

  • Due to the complexity of modeling and transformation, today’s networked digital twins tend to focus on one specific use case data across domains. Teresa Tung, chief cloud specialist at Accenture, said the new knowledge graph technology is helping connect the dots. For example, a digital twin of a network can combine models from different domains, such as engineering R&D, planning, supply chain, finance, and operations.

    They also bridge the workflow between design and simulation. For example, Accenture enhanced traditional network planning tools with new 3D data and RF simulation models to plan 5G deployments.

    Connect2Fiber is using a digital twin to help model its fiber optic network to improve operations, maintenance and sales processes. Nearmap’s drone management software automates an inventory of wireless infrastructure to improve network planning and collaboration processes through digital twins of assets.

    These efforts can all benefit from innovations driven by Building Information Modeling (BIM) in the construction industry. Comparable Network Information Models (NIMs) could have a similarly transformative role in building complex networks, predicts Jacob Koshy, information technology and communications associate at IT services firm Arup.

    For example, RF propagation analysis and modeling for coverage and capacity planning can be reused during system installation and commissioning. Additionally, integrating components into a 3D modeling environment can improve collaboration and workflow between facility and network management teams.

    Emerging digital twin APIs from companies like Mapped, Zyter, and PassiveLogic may help bridge the gap between dynamic networks and the built environment. This makes it easier to create comprehensive digital twins that include the cyber aspects involved in more autonomous business processes.

    The future is autonomous network

      Grammer believes that improving the integration between digital twins and automation can help fine-tune conditions based on change. For example, commercial traffic may dominate during the day and shift towards more entertainment traffic at night.

      “With these new modeling tools, the network will be able to automatically adapt to changes in applications, easily switching from business videoconferencing profiles to streaming or gaming profiles,” Grammer said.

      How will the digital twin Optimizing Network Infrastructure

    The most common use case for digital twins in network infrastructure is testing and optimizing network device configurations. In the future, they will play a more prominent role in testing and optimizing performance, reviewing security and compliance, configuring wireless networks, and rolling out large-scale IoT networks for factories, hospitals, and warehouses.

    Experts also want to see more direct integration into business systems such as enterprise resource planning (ERP) and customer relationship management (CRM) to automate the rollout and management of networks to Support new commercial services.

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