Learn the basics of edge computing and how it is transforming the realtime landscape
The ‘edge’ refers to computing infrastructure that exists close to the origin sources of data. It is distributed IT architecture and infrastructure where data is processed at the periphery of the network, as close to the originating source as possible.
Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data.
Living on the Edge
A series of gateway servers sit outside your primary cloud environment, allowing for more localized data processing.
Examples of edge computing can be found throughout our everyday lives — we just may not notice them.
Industrial Internet of Things (IIoT)
- Wind turbines
- Magnetic resonance (MR) scanner
- Undersea blowout preventers
- Industrial controllers such as SCADA systems
- Automated industrial machines
- Smart power grid technology
- Smart streetlights
Internet of Things (IoT)
- Motor vehicles (Cars and trucks)
- Mobile devices
- Traffic lights
- Home appliances
Edge Computing Benefits
Edge computing allows for the clear scoping of computing resources for optimal processing.
- Time-sensitive data can be processed at the point of origin by a localized processor (a device that has its own computing ability).
- Intermediary servers can be used to process data in close geographical proximity to the source (this assumes that intermediate latency is okay, though realtime decisions should be made as close to the origin as possible).
- Cloud servers can be used to process less time sensitive data or to store data for the longterm. With IoT, you’ll see this manifest in analytics dashboards.
- Edge application services significantly decrease the volumes of data that must be moved, the consequent traffic, and the distance the data must travel, thereby reducing transmission costs, shrinking latency, and improving quality of service(QoS) (source).
- Edge computing removes a major bottleneck and potential point of failure by de-emphasizing the dependency on the core computing environment.
- Security improves as encrypted data is checked as it passes through protected firewalls and other security points, where viruses, compromised data, and active hackers can be caught early on (source).
- The edge augments scalability by logically grouping CPU capabilities as needed, saving costs on realtime data transmission.
Why the Edge
Transmitting massive amounts of data is expensive and taxing on network resources. Edge computing allows you to process data near the source and only send relevant data over the network to an intermediate data processor.
For example, a smart refrigerator does not need to continually send internal temperature data back to a cloud analytics dashboard. Rather, it can be configured to only send data when the temperature has changed beyond a particular point; or, it could be polled to send data only when the dashboard is loaded. Similarly, an IoT security camera could only need to send data back to your device when it detects motion or when you explicitly toggle a live data feed.
Device Relationship Management (DRM)
To manage edge devices, device relationship management (DRM) refers to the monitoring and maintenance of complex, intelligent, and interconnected equipment over the internet. DRM is specifically designed to interface with the microprocessors and local software in IoT devices.
Device relationship management (DRM) is enterprise software that enables the monitoring, managing, and servicing of intelligent devices over the Internet.
Between the edge and cloud is the fog layer, which helps bridge the connections between edge devices and cloud data centers. According to Matt Newton of Opto 22:
Fog computing pushes intelligence down to the local area network level of network architecture, processing data in a fog node or IoT gateway.
Edge computing pushes the intelligence, processing power and communication capabilities of an edge gateway or appliance directly into devices like programmable automation controllers (PACs).
Edge and Realtime
Sensors and remotely deployed devices demand realtime processing. A centralized cloud system is often too slow for this, especially when decisions need to be made in microseconds. This is especially true for IoT devices in regions or locations with poor connectivity.
However, sometimes realtime capabilities demand cloud processing. For example, lets say data consumed by remote tornado weather monitors needs to be sent in realtime to massive supercomputers.
This is where realtime infrastructure comes into play to help enable those data transactions.