What is a network digital twin? And why is it relevant to AIOps?

The term ‘digital twin’ became popular well before AI took off. For years, network operators have been using digital twin capabilities to model and verify network behavior – minimizing network emulation and testing in the lab, which can be prohibitively expensive, power-hungry, and time-consuming. Ciena’s Mark Bieberich explores how network digital twins are even more relevant in the context of an AIOps strategy.

In the networking industry, reliability is of utmost importance, requiring network products and solutions to be verified rigorously prior to live deployment. This includes testing in lab environments that are representative of the network. For decades, vendors and service providers committed substantial resources to replicating (a subset of) network conditions to help ensure equipment and software perform to expectations and enable high-quality service for end users. However, given the practical constraints of budget and time, limited computing power and availability, and ever-growing network complexity, such physical environments can never fully replicate the network, leading to a risk of test escape and potential outages in a live deployment.

As processing power and software capabilities advanced in the early 2000s, it became feasible to replicate all the characteristics of physical entities in virtual form as a digital twin. A digital twin could be kept in sync with the actual network via interactive data updates, real-time and periodic. When applying the digital twin concept to networking, the IETF has a work in progress “Network Digital Twin: Concepts and Reference Architecture,” which notes that there is no standard definition and states: “This document introduces five key elements (i.e., data, models, mapping, interfaces, and logic) to characterize the Network Digital Twin. These five elements can be integrated into a network management system to analyze, diagnose, emulate, and control the real network.”

At Ciena, our definition is along similar lines: a network digital twin is a virtual representation of all the details of the real-world physical network (network elements, configurations, connectivity, and behaviors). It may be kept synchronized with the physical network using real-time streaming data or “snapshots” of configurations and traffic data. In either use case, the network digital twin is used to simulate, analyze, and optimize physical network behavior.

The benefits of a network digital twin are many. It serves as an accurate, risk-free environment in which to run what-if scenarios to verify optimal network and service performance under varying conditions for typical operational use cases:

• Capacity planning: network buildouts can be designed and analyzed for key metrics (such as space, power, bandwidth, resiliency) under diverse traffic patterns prior to actual ordering, installation, and commissioning of equipment
• Service provisioning: back-end integration with operational support systems (OSS) and automation of provisioning steps can be pre-checked prior to exercising end-to-end workflows in the real environment
• Network optimization: paths should be traffic engineering first in the digital twin and load-tested, to ensure service level agreements can be satisfied
• Network and service assurance: simultaneous faults can be simulated and resulting behavior can be analyzed to ensure that the network is multiple-fault tolerant
• Troubleshooting: in the event of actual network faults, recovery actions can be pre-verified in the digital twin environment before applying them to the network
• Capacity management: utilization trends can be analyzed and high data traffic scenarios can be simulated to ensure that the network can gracefully withstand heavy load

And let’s not forget sustainability. Given the importance of sustainability initiatives, operators can replace some of their hardware-intensive labs with more energy-efficient software-powered digital twins, helping lower an organization’s carbon footprint.

Augmenting the capabilities of digital twin with AI

With the rise of AI innovation, network digital twins have another important role. When designing generative AI (GenAI) applications powered by large language models (LLMs), which generate content from a variety of sources, it is imperative to stringently evaluate the accuracy of the model output. Within a digital twin, LLMs can be safely evaluated using validation datasets with no adverse impact on the real-world network. The LLM training and validation processes can then be reiterated until the desired model accuracy is achieved and the application can be deployed.

Looking at it from another angle, AI can also improve the capabilities of the digital twin itself. That is, Machine Learning (ML) algorithms can augment the digital twin’s emulation and predictive analysis capabilities. This is especially useful for optimization and planning use cases that draw on large datasets to predict network behavior, so that the network can be adapted to maximize performance. As AI gets more integrated with network operations, network digital twins will become more interwoven within the overall AIOps strategy.

As the business benefits of employing digital twins become clearer, we see increasing interest in leveraging digital twins to plan, analyze, and operate increasingly complex networks. To that end, Ciena’s software portfolios – Blue Planet and Navigator Network Control Suite (Navigator NCS) – support a wide range of network digital twin use cases.

Blue Planet’s Route Optimization and Analysis (ROA) is uniquely suited for dynamic, multi-vendor IP network use cases. To support these complex environments, ROA provides real-time what-if analysis and ‘network DVR’ capabilities to simulate how planned network configuration changes, or various fault scenarios, affect real-world IP network traffic and performance. This insight helps operators optimize the customer experience. Emulation Cloud, part of Navigator NCS, provides a cloud-hosted environment that simulates multi-layer networks comprised of Ciena optical and routing and switching platforms. Via remote access, operators can rapidly plan network buildouts and verify configurations, as well as analyze network performance trends and test-drive optimization measures. Emulation Cloud can also help accelerate the adoption of advanced AI apps by validating AI model outputs.

Stay tuned for our future blogs, where we will delve deeper into ROA and Emulation Cloud's network digital twin capabilities and demonstrate how these complementary solutions use AI to help operators tackle increasing network complexity and improve their operations.