Digital Twins and Their Potential on IaaS
The ability to predict is becoming increasingly important. We need information for predictive maintenance to prevent machine failures. We need information on market developments to manage our investments correctly.
But predictions can go even further. Imagine that you are an aircraft designer preparing a prototype of a new wing shape. Its design will contribute to 15% fuel savings. The wing flies, brings savings but suddenly it causes the plane to crash. There was a design flaw but it was hidden under standard conditions.
Fortunately, you simulated this scenario using a digital twin of the wing. The disaster happened only on your monitor screen and you have the opportunity to prevent it in reality without having to sacrifice the efficiency of your new invention.
We have just been introduced to the concept of a digital twin. In this article, we will take a closer look at what it actually is and how it helps to predict operations and functionality in various industries, sometimes even in entire cities.
Digital Twins 101
A digital twin can be thought of as a living, adaptable model of a real object or system. This model is physically an exact replica of its real counterpart, and the same principle is replicated by the conditions that we can set within the simulated environment.
The difference from conventional modeling is the part about simulating conditions. While standard modeling is about accurately capturing an object, a digital twin is about how such an accurate model behaves under specific conditions.
Why Is There So Much Talk Around Them?
Digital twins bring one huge advantage which has been mentioned at the beginning of this article. It is possible to test behavior under different conditions without having to manufacture a single component or operate a real system.
Thanks to this it is possible to carry out real-world testing in a virtual environment and incorporate feedback from operations before the object or system ever enters it.
Industry has long used this approach to improve the architecture and operation of production lines (for example, Foxconn). A smart turbine can be used for the production and operation of wind farms. This model helps to operate wind power plants efficiently and also to perform predictive maintenance which reduces the costs of necessary repairs and shutdowns.
Another interesting example is the digital heart managed by Johns Hopkins Hospital and the Cleveland Clinic. Thanks to it it is possible to simulate the consequences of treatment for specific cardiac problems.
But in the world of digital twins, it is not just about working with one object. In Singapore or Hong Kong, they model the operations of an entire city. This allows them to better plan new projects within the Smart Cities program and optimize, for example, water or waste management or infrastructure construction planning.
Why do digital twins need a strong infrastructure?
A digital twin is a model of something real, embedded in a simulation of real conditions. This means that it needs data – lots of data – to function properly. Data streams are used to make correct calculations, subsequent predictions and create automations.
Such a process is extremely demandingm for computing power. While it is possible to create 3D models on a computer, digital twins need infrastructure for the most demanding ones. The volume of data is not constant, so twins require not only a robust infrastructure but also one that can be efficiently scaled.
Where can you run digital twins?
This will always depend on the specific case. They can run on any type of infrastructure that covers their computing needs. This can be physical servers, private cloud, hybrid cloud, virtual servers, IaaS or various combinations.
A much better question, however, is: where is it appropriate to run digital twins? Then there are not so many options.
When not to go the hyperscaler route?
AWS, Azure and Google are quite often the first choice for most companies. Their big brand and wide range of services make them obvious candidates. However, especially with regard to large amounts of data and large data flows, these global hyperscalers may not be able to handle it very well.
Their pricing models are partly based on fees for transferred data. So it would not only be about computing resources, but also the costs for data transfer, which may not be negligible in the case of digital twins.
For some digital twins, it also depends on the latency with which data is processed in the cloud environment. In this case, placing the infrastructure as close as possible to the points that send the data plays a vital role.
Then there is another consideration that has been scaring everyone lately. These are legislative requirements for data security and privacy. GDPR and NIS2 place high demands on the proper handling of entrusted data, and in some cases, using an American provider may not only be counterproductive, but directly going against these regulations.
IaaS as the foundation for digital twins
Digital twins require high computing power, high flexibility, isolation of the environment of individual systems and the ability to scale on the fly. These conditions must be met without provider intervention, in a secure environment and with full control over the entire environment in the hands of the user.
However, such requirements can come at a pretty high cost in the form of physical infrastructure. So how do you combine high demands for the environment properties with optimized costs? The answer is Infrastructure as a Service (IaaS).
This type of environment reduces the cost of acquiring physical IT equipment to zero, while not depriving digital twins of any of the features mentioned in the previous paragraph. However, not every IaaS platform meets these conditions.
The properties of such a service are determined by the virtualization platform on which it runs. IaaS running on OpenStack is very suitable for robust and demanding environments, such as digital twins. Let’s look at why.
Segmentation by fields of use
Digital twins in manufacturing, where high customization and network control are needed
Digital twins in manufacturing are commonly used today for simulating production lines, managing energy consumption or machine predictive maintenance. In order for such models to operate reliably and in real time, they need an infrastructure that can process operational data with low latency.
IaaS built on OpenStack offers exactly what manufacturing operations need in this regard – the ability to manage their own networks, allocate public IP addresses, isolate individual environments, or work with their own images.
At the same time, it is possible to easily connect edge devices or OT systems to this environment using VPN or SD-WAN, which is essential for connecting the digital twin to real production.
Such an approach is ideal when you need a combination of high flexibility, network control, and security – and at the same time not wanting to bear the costs of and responsibility for your own physical infrastructure.
Digital twins using AI/ML models
Digital twins using artificial intelligence are used wherever there is a need to recognize patterns, predict failures or visualize complex operational data.
In order for these scenarios to work reliably and effectively, they need a computationally powerful and flexible environment. IaaS built on OpenStack meets this need – it can offer GPU instances with accelerators. Thanks to this, demanding calculations can be scaled according to current needs.
Kubernetes container environments are increasingly becoming a popular way to run applications. This gives users full control over the deployment of AI models and their full-cycle operation.
Kubernetes is compatible with OpenStack virtualization and neither of these tools contains the pitfalls associated with vendor lock-in. For simple deployment, it is possible to use an automation platform (more information can be found here).
Urban digital twins and geographic simulations
Urban digital twins are used to simulate traffic flows, model floodplains, or monitor energy flows in buildings. These scenarios work with a huge amount of spatial data – from 3D models to point clouds to historical records.
OpenStack IaaS offers an ideal environment for such cases thanks to its support for object storage technologies such as Swift or S3-compatible storage, which can store and serve large data sets.
It also allows easy connection to GIS systems and visualization tools via REST API, so data from the digital twins can be immediately available for analysis and planning teams.
For cities, developers, or infrastructure managers, OpenStack IaaS is the ideal choice if they want to run advanced simulations in a scalable, flexible, and fully controllable environment.
Scenarios with high security or compliance requirements
In areas such as healthcare, energy or public administration, digital twins are a sensitive component of operations. It can be a model of a hospital system or treatment procedure, network management of a power grid or simulation of the impacts of interventions in urban infrastructure.
In such cases, not only are performance and availability crucial, but also legal certainty and control over data. OpenStack IaaS, operated within the EU, meets these requirements – it supports GDPR, runs in a data center located in the EU and often also meets standards such as ISO 27001 (NIS2 is based upon this standard).
At the same time, it provides the ability to operate systems in separate user environments, which ensures a high degree of isolation and security – without the need to share computing resources with other customers.
This makes OpenStack IaaS an ideal choice for organizations that must comply with strict regulatory frameworks and cannot (or must not) use hyperscalers such as AWS or Azure, for example due to data sovereignty. It allows the operation of critical digital twins securely, locally and under full contractual and technical control.
Low budget/high frequency simulations where hyperscalers are not cost effective
In some cases, digital twins are deployed at large scale for repetitive computations – for example, during design optimization, batch execution of simulation scenarios, or as part of a CI/CD pipeline for model development and testing.
These situations emphasize efficient performance scaling and transparent costs, as the computational workload is often intermittent but intensive.
In this context, OpenStack IaaS offers an attractive alternative to classic hyperscalers, as it allows you to use exactly the type of instances that the task requires.
This allows you to significantly optimize costs while avoiding complex licensing models or unpredictable billing. Especially in cases where you need to regularly run a large number of simulations and pay only for actual usage, OpenStack IaaS is one of the most effective solutions.
Summary – When is OpenStack IaaS the best choice?
Scenario
- Industrial / Manufacturing Twins
- AI/ML Digital Twins
- City / Geographic Twins
- Regulated Industry
- Cost and Control
Benefit Reason
- Custom Network Topology, Project Isolation
- GPU Instances, Custom Orchestration with No Lock-In
- Object Storage, Data Flexibility
- GDPR, ISO, EU Operation, Sovereignty
- Cheaper Than Hyperscalers, Transparent Billing
Why does it make sense to build digital twins on OpenStack IaaS?
If IaaS running on OpenStack is the right choice for your digital twin, then such an environment will bring you
- Full control over the environment architecture
Computing resources dedicated to your needs - Transparent billing
- Scaling exactly as needed
and many other benefits that will be specific to your use case or POC.
Are you considering where to run the digital twin of your system or device?
Our infrastructure built on OpenStack runs exclusively in the EU, offers performance, scalability and full control over data – without vendor lock-in.
Get in touch with us – we will be happy to design the optimal environment for your use case.
