- The industrial edge is thriving despite the cloud computing hype
- The unique requirements of industrial operations
- The decentralised nature edge computing
By Rohan de Beer End User Sales Director, Schneider Electric
Despite the rapid rise of cloud computing, driven largely by the hype surrounding key factors like skills, budget, and security, the edge has Edge computing is essential to industrial operations because it can support the real-time and mission-critical requirements of industrial settings. The cloud cannot often match these demands. Since the advent of cloud computing many organisations have realized that not all workloads and applications can be moved to cloud due to legacy systems and compliance issues, security and performance issues.
In many cases, the application determines whether or not it can be moved into the cloud. This is where latency becomes a factor in industrial settings. It makes sense to move the data closer to the application if it needs a faster reaction time.
This happens because tasks that require Artificial Intelligence, automation and quick reactions benefit from computing power near the source of data. This reduces latency and enhances decision-making speed. Remote mining operations
For example, mining operations are often located in rural or remote locations where there is some latency in the line to the large data centre. An edge node, or data centre, is often set up near the mine to manage everything. The IT team can then replicate data into the cloud each day.
Edge computing’s decentralised nature enhances industrial systems’ reliability and resilience by distributing data processing over multiple edge devices. This reduces dependency on a single central server and eliminates single points of failure. This local data processing ensures critical functions can continue without interruption even if the connection to the central server is lost.
Edge computing also allows for better load-balancing, which prevents any one device from becoming overwhelmed, leading to system failures. Edge computing is therefore crucial for Industrial IoT applications (IIoT), which generate large amounts of sensor data. Edge computing is also ideal for real-time AI applications and machine learning (ML), such as predictive maintenance and quality control, and process optimization. Edge computing reduces the need for data to be sent to centralised servers because it processes data locally, on devices or close to the data source. This reduces the latency and allows AI and machine-learning models to make real time decisions.
Rohan De Beer
Efficient Use of Resources
By analyzing data at the edge, computing resources are used more efficiently. This is especially important for AI applications which require significant processing power such as predictive maintenance.
Real time AI can analyse sensor data in order to predict equipment failures, reducing downtime and maintenance costs. This ensures that equipment is operating efficiently. Edge computing optimises bandwidth and reduces costs by sending only relevant data to the cloud.
Edge computing has several attributes that make it indispensable for modern industrial IT architectures. These include resilience, scalability, and security. Edge computing is essential for modern industrial IT architectures. It enables more efficient, secure, and responsive operations.
Edge computing is here to remain and the architecture that surrounds it will continue to evolve as technology advances. Schneider Electric’s EcoStruxure IT Data Centre Infrastructure management 3.0 architecture is well suited for distributed industrial environments. It offers unparalleled monitoring and management of hybrid IT environments and ensures operational continuity and security.
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