As Edge Computing grows in popularity, demonstrating an ongoing evolution in system architectures, where does that leave the Cloud? Operational Technology must move to the edge.
Where compute functionality occurs has shifted from centralized to distributed over the past few decades. We’ve come a long way from platforms like mainframes and thin clients. Edge Computing is growing in popularity, marking another step in an ongoing shift and evolution in system architectures. Where does that leave the Cloud? The answer is: it depends. I’m going to explain why…
The inception of Industry 4.0 brought with it software-defined everything. This latest industrial revolution was expected to deliver even more automation than the third revolution by bridging the physical and digital worlds. This required moving from centralized, fixed industrial controls to those that could adapt to changing market needs and/or feedback from the environment itself – software-defined systems. The Programmable logic controllers (PLCs) — originally imagined as a physical input with Inputs / Outputs (IOs)– were now a container workload on a large platform. At a macro level, this impacted how humans interacted with the physical and digital worlds. Machines made up of dedicated controllers (not updated or changed) would now be driven by software-defined industrial PCs that can both drive the machines and understand and adapt to their surroundings.
We’re in the Cloud Wars as Cloud providers continue to innovate on the technology. If we exclude China’s Alibaba, Baidu, and Tencent, in North America and Europe alone, the three leading Cloud providers are continuously pushing forward innovative and complete products. Leaders in this space recognized the concern from end customers of being tied into a single Cloud provider. As an example, Google’s Anthos software platform, announced in 2019, offers a single, consistent way of managing Kubernetes workloads across on-prem and public cloud environments.
For the Operational Technology (OT) executive, connectivity to this type of functionality offers appealing prospects for system effectiveness through access to several services, including streaming analytics, data lakes, data storage, monitoring, and IoT security management. We are hearing from customers that implementing similar functionality on-prem can be two to three times more expensive. We believe that the cost gap will continue to grow.
IT organizations in almost every industry are transitioning – or are in the process of transitioning – to leveraging Cloud services. However, OT operators have been slow to adopt Cloud-based techniques. While moving to the Cloud may relieve the OT operator of maintenance tasks like provisioning, installation, updates, and patches, the ability to maintain control and limit the threat of cybersecurity vulnerabilities is too appealing. It’s often ingrained in OT leaders to stay away from the influence of IT organizations and remain independent for procurement support and management of their technology infrastructure.
For some operators, moving to the Cloud could simplify their operations, allow flexibility in scaling, and avoid increasing cost. In the manufacturing industry, we have seen more in the public domain from Microsoft and its customer base that builds on a foundation of decades of business and supplier familiarity around Windows technology. This has been initially focused on predictive maintenance and quality improvement use cases.
- Food industry and packaging pioneer Tetra Pak employs new, digital tools that enable its Cloud-connected machines to predict exactly when equipment needs maintenance. Through the use of Microsoft Azure Cloud, Tetra Pak can collect operational data to help predict informed maintenance timing by connecting packaging lines to the Cloud.
- Competent and cost-effective artificial intelligence (AI) has allowed manufacturers to maintain quality in high-volume manufacturing environments. Operators can analyze camera feeds in real-time to have faulty widgets identified and tagged either physically or virtually. AI also enables manufacturers to inspect every part coming off the line, which has never been practical or economical with a human workforce. We see this solution’s value in a number of verticals, especially in manufacturing complex automotive components – price-sensitive, high volume, and commonly safety-critical.
While Cloud operators offer various IoT strategies intended to address these concerns, OT operators still see a gap between what is needed to meet their requirements and the available architectures. Fortunately, new architectures can allow the operators to have their cake and eat it too. The choice of the right system architecture will ensure that their current operations are not impacted while also benefiting from a number of data-based optimization, including:
- Once software and hardware are decoupled, cost of maintenance and upgrades decreases significantly.
- Systems can be more flexible and respond to changing requirements with significantly lower cost, risk, and time.
- Systems become observable, which opens up the ability to collect data, deliver unique insights and closed-loop optimizations.
The challenge lies in delivering these capabilities while also maintaining the vitally essential attributes of the OT network, like system uptime, deterministic real-time functionality, and immunity to cyberattacks.
This architecture is known as “Mission Critical Edge.” It securely combines the scaling benefits of IT infrastructure with the reliability, deterministic real-time behavior of embedded platforms. Attributes include:
- Airgapping: System architects must precisely define and dedicate CPU, memory, and IO resources to specific virtual machines (VMs). By isolating VMs from each other (including the northbound and southbound connectivity), OT and Cloud applications can reside on the same system.
- OT Manageability: The system should be flexible on the management and control of the configuration and setup. This allows the system to be managed locally while enabling specific workloads to be updated and managed by the Cloud.
- Performance: Real-time performance must be guaranteed for the workloads such as PLCs, PACs, and ECUs. The system hosting the Cloud workloads on the shop floor can also have a dedicated partition that can be the backup for a physical PLC.
- High Availability: Mission-critical edge enables high availability implementation at different levels, within a single system, across two systems in a cell, and across an entire production line.
- Orchestration Framework Integration: The edge systems need to be able to work with either a local or Cloud-based management framework. For example, systems across a factory should dedicate a portion of their workload to form Kubernetes clusters.
The mission-critical edge architecture can revolutionize the entire OT experience. By enabling the edge systems on the factory floor to run multiple air-gapped workloads, including real-time, AI/ML, security, etc., OT operators can deploy Cloud-connected services and workloads on their factory floor without affecting their current operations. In addition, the air-gapped workloads can be combined to run Kubernetes orchestrated container workloads.
