Despite the rapid growth of cloud computing, industrial edge remains a core component of industrial IT environments.
The hype around key factors like skills, budget and security continues to drive cloud adoption. However, unique industrial requirements ensure edge computing remains integral by supporting real-time, mission-critical demands that the cloud often cannot match.
Since cloud computing emerged, many organisations have realised not all applications and workloads can move to the cloud.
Thriving in the shadow of cloud computing
Legacy systems, compliance, security, performance and latency issues prevent full cloud adoption in many cases. The application usually determines whether it can move to the cloud, bringing latency into question in industrial settings.
If an application requires faster response times, moving data closer to where it is needed makes sense. Tasks requiring AI, automation and swift reactions benefit from computing power near the data source. This reduces latency and enhances decision-making speed, improving efficiency in industrial operations.
Remote mining operations
Mining operations are often in remote areas where latency exists on the line to a big data centre. An edge node or local data centre near the mine often manages operations.
The IT team can then perform data replication into the cloud at the end of each day. Edge computing’s decentralised nature enhances reliability and resilience by distributing data processing across multiple edge devices. This reduces dependency on a central server and eliminates single points of failure.
Local data processing ensures critical functions continue uninterrupted, even if central server connectivity is lost. Additionally, edge computing improves load balancing, preventing system failures from overburdened devices. This makes edge computing crucial for Industrial IoT (IIoT) applications generating massive sensor data volumes.
Real-time AI and ML
Edge computing enables real-time AI and Machine Learning (ML) applications like predictive maintenance, quality control and process optimisation. It facilitates intelligent, immediate decision-making by processing data locally or near the source.
Reducing reliance on centralised servers lowers latency, allowing AI and ML models to make real-time decisions.
Efficient use of resources
Processing data at the edge optimises computational resources, which is vital for AI applications requiring significant processing power.
For example, real-time AI analyses sensor data to predict equipment failures before they happen. This minimises downtime and maintenance costs, ensuring equipment operates efficiently.
By sending only relevant data to the cloud, edge computing reduces bandwidth usage and associated costs.
The future of edge computing in industry
Edge computing provides resilience, scalability, and security, making it essential for modern industrial IT architectures. These attributes make edge computing indispensable, ensuring more efficient, secure and responsive operations.
Edge computing is here to stay, and its architecture will continue evolving as technology advances. Our EcoStruxure IT Data Centre Infrastructure Management (DCIM) 3.0 suits distributed industrial environments. It offers unparalleled monitoring and management for hybrid IT environments, ensuring operational continuity and security.
Rohan de Beer | Sales Director | End User | Schneider Electric | mail me |
