Recent technology innovations have made real-time monitoring of live systems easier and more powerful. Digital twins are one of the latest game-changing technologies.
The Digital Twin Consortium defines digital twins as virtual representations of real-world entities and processes, synchronised at a specified frequency and fidelity. They use real-time and historical data to represent the past and present and simulate predicted futures.
Michael Grieves first introduced the concept of digital twins in 2002 for use in manufacturing and product lifecycle management. Digital twin use cases have now expanded to include real-time monitoring for large systems with many entities. Applications include smart city device tracking, helping prevent train derailments, and managing complex airline and logistics operations.
Digital twins can both monitor and simulate live systems
Traditional streaming analytics typically perform only rudimentary analysis in real time and develop the bulk of their insights offline with big data tools. Digital twins move the analysis to real-time by simultaneously tracking thousands of data sources and immediately identifying actionable issues. To accomplish this, they combine incoming telemetry with relevant contextual information that enriches their real-time analysis.
Digital twins leverage in-memory computing technology to provide the fast, scalable performance they need to perform real-time monitoring for thousands of data sources. This technology hosts digital twins in memory for fast access and triggers analysis of incoming telemetry within 1-3 milliseconds. It scales performance by distributing digital twins across a cluster of physical or virtual servers and seamlessly increasing throughput as the workload grows.
In addition to performing real-time monitoring, digital twins can simulate large systems with thousands of entities. This enables predictive modeling to assist in making design choices and help operational managers optimize the behavior of live systems. For example, digital twin simulations can help airline dispatchers evaluate the impact of delaying departures when responding to unexpected weather delays.
Open-source APIs and development tools add flexibility
A key challenge in the evolution of digital twins has been the lack of software APIs and open-source tools for implementing them. To address this problem and help accelerate application development, ScaleOut Software has released open-source APIs and workbench for developers. These APIs take advantage of an object-oriented software architecture for creating digital twins. They define a simple but powerful technique for encapsulating contextual information and processing incoming messages. This software architecture provides a concrete model to help catalyze the use of digital twins in a wide variety of applications.It enables the implementation of digital twins for both real-time analytics and the simulation of large systems.
All developers know that the process of testing and debugging a new application can require repeated testing and bug-fixing. ScaleOut Software’s open-source workbench shortens this development cycle by enabling developers to test digital twin applications in their familiar development environment. It integrates with popular development tools, such as Visual Studio and IntelliJ.
Once fully tested, digital twin models can be deployed to a production platform for real-time monitoring and simulation at scale. For example, ScaleOut’s production platform, ScaleOut Digital Twins™, hosts thousands of digital twins and incorporates powerful facilities for data aggregation and visualization.
The future of informed decision-making
Mission-critical applications that track many live data sources need to quickly sift through incoming telemetry, assess dynamic changes, and immediately pinpoint emerging issues that need attention. By enabling real-time monitoring and predictive modeling for large, complex systems, digital twins give operational managers important new tools to boost their situational awareness and make better decisions. We expect developers to use digital twins in addressing many complex challenges, such as optimizing traffic flows in smart cities, preventing train derailments, and managing unexpected issues in airline operations.
ScaleOut’s open-source APIs and workbench give practitioners a template for implementing digital twins and putting them to work. These software tools should accelerate the use of digital twins in tracking live systems and enable organizations to explore innovative ways to address their business challenges.
(Editor’s note: This article is sponsored by ScaleOut Software)
