With their numerous machines and integrated sensors, production environments such as production lines deliver masses of data every day that are suitable for much more than controlling the current production process. Much of this data is not actively analyzed today due to missing or incomplete data models. In order to be prepared and to avoid losing data, too much data tends to be stored. Data lakes are a partial solution to this, but they are also associated with costs.

Discussions with data analysts revealed that they do not always receive all the data they need. One of the reasons for this is the growing volume of data and the associated increase in network capacity utilization. However, industrial companies want to evaluate this data in real time. Public clouds initially offered a quick solution - you simply send all the data to the cloud for data analysis. Depending on the use case and data volume, this approach works quite well. However, other use cases with growing data volumes require a combined edge/cloud-based infrastructure in terms of data security, latency and real-time processing.

Data evaluation in the Edge

The solution: analytical data reduction and evaluation (ADA) directly on the production line in a standards-based edge device, without having to forego the benefits of a cloud environment. This means that companies evaluate the data at the point of origin in the edge device and decide on site where the data should be sent. This can be done using a machine learning algorithm or proprietary container-based applications.

'Ready to go' cloud box solutions can do exactly this in conjunction with modern container technologies. This gives users more control over their data, reduces data volumes and increases security.
Once the machine learning model has been created on the basis of current production data, it starts working directly at the edge. In conjunction with modern integration bus technologies, only the relevant data is forwarded to the central data analysis platform, where it is evaluated with other company data. If desired, the unused 'raw data' or parts of it can be stored or buffered decentrally. In addition to the greatly reduced amount of data, this architecture is also a major security benefit, as security-relevant data can be encrypted at the point of origin or forwarded or deleted accordingly.

Current ADA applications based on container technology are flexible by design and can be adapted as required. The number, size or location of the cloud instances is irrelevant; centralized management is almost always possible. This also works with a combination of one or more public or private cloud environments.

The planning decides

As with everything, planning is important. If certain rules are taken to heart when developing the solutions that will later run in Docker containers, there are virtually no limits to flexibility. The containers run on almost all current cloud infrastructures (on-prem, off-prem, public or private). Solutions from the areas of multi-cloud management, cloud automation and monitoring round off the picture of such a multi-cloud environment as efficient to manage. The edge components, based on standard cloud technologies, are also integrated here.

With the 'Cloud-in-a-Box' ADA-EC platform, IBM has presented a ready-to-go cloud technology, as shown here for the first time in May 2019 at Think at IBM in Berlin.

© IBM

Even though there is great awareness of the importance of production process data, many companies are reluctant to take the step of using the data for further analysis. One reason for this is a lack of expertise in container management such as Kubernetes. Basic knowledge of Docker is often already available and is being built up further. These companies have started to develop Docker-based solutions and are gradually facing up to the challenges:

- As the number of Docker containers increases, companies have realized, for example, that they need Docker management like Kubernetes.

- The solutions initially developed purely on Docker work, but are only scalable to a very limited extent or at high cost.

- As the number of applications increases, an app catalog is also required so that the ready-made solutions and updates can be installed and used via push or pull.

Creating, distributing and operating such infrastructures yourself from individual open source components can still work in a manageable environment. With a larger number of applications, edge systems and IoT devices, this quickly becomes a mammoth task.

Suitable infrastructure for rapid deployment

A secure, fully functional cloud should be small in order to bring more computing power directly to the machines or to the store floor. The cloud-in-a-box solutions that are currently ready for use bring a fully functional cloud with the necessary computing power to the end device, enabling machine learning models to be executed on site. The results flow directly into the further development and optimization of the models - creating a cycle. These standardized cloud boxes have been specially developed for entry into AI-supported manufacturing solutions. This allows companies to expand and develop their ecosystem in their core business.

All of IBM 's cloud solutions are based on Red Hat Openshift, can run on all common cloud environments and can be managed centrally.

© IBM

The core element is a cloud platform based on open source frameworks such as Docker and Kubernetes with an app store. The container-based applications and updates can be compiled and distributed via this company-owned app store. In order to exploit all the advantages of a private cloud environment, the creation of machine learning models and the database used for this must also be considered. This part of the machine learning lifecycle is also part of the cloud-in-a-box solution in the production environment. The respective provision model differs here on the market. Ideally, companies that have their data analyzed in real time on site should not have to send it to a provider or a public cloud every time the machine learning model is adapted, supplemented or changed.