Edge and fog computing are closely related – both refer to the ability to process data closer to the requester / consumer to reduce latency cost and increase user experience. Both are able to filter data before it “hit” a big data lake for further consumption, reducing the amount of data that needs to be processed. The basic idea of edge and fog computing is to move data logic (mainly around data validation / data grammar checks) to an outer ring of capabilities.
Edge and fog computing concepts have been developed to respond to the cheer increase of data bandwidth required by end devices and has been fuelled by the explosion of IoT (Internet of Things) which in turn has increased the need to process the generated data much closer to the source in real time. In other words, edge and fog computing push the cloud (read data centre) closer to the requester to minimise latency, minimise cost and increase quality.
For example, a Boeing 787 generates 40 TB per hour of flight, but just half a TB of this is ultimately transmitted to a data centre for analysis and storage. Similarly, a large retail store might collect approximately 10 GB of data per hour, but just 1 GB of that is transmitted to a data centre. As it is not sensible, nor possible to install a full data centre either on a plane or within a store, edge or fog computing steps in to validate and pre-process this data either within a local network (fog) or a gateway device (edge).
And this is the main difference between edge and fog computing – the location of the devices. Fog computing pushes the data validation intelligence further into the local network, whereas edge computing places that data validation and processing intelligence onto central edge devices like routers and switches.
Cloud and edge / Fog computing cater for a different set for requirements as set out in the comparison table below
IoT devices are “chatty”, they produce a constant stream of data that has to be validated, analysed and processed. Traditional transactional systems had the requester (say a client application) sending not validated data to a data processor that was typically installed in a central data centre. With the explosion of IoT devices the data validation / data grammar checks have to happen closer to the request
What are the advantages of edge and fog computing for the industrial internet of things (IoT)?
- Edge computing is a direct response to the monumental increase of bandwidth required by the end devices that underpin the IoT. These devices are ‘chatty’, and produce a constant stream of data that has to be validated, analysed and processed in real time to provide an excellent end user experience.
- As edge and fog computing pushes the data validation closer to the requester, it can crunch through data at a faster pace than if it were held in a central location. It also allows for offline or disconnected validation of data, which reduce the total amount of end to end bandwidth needed, thus lowering bandwidth costs, too.
- Edge computing can also bolster cyber defences, as various security measures such as encryption can be implemented in the local network before the data traverses through unprotected parts of the internet.
What are the disadvantages ?
- Edge and fog computing can add a layer of complexity to the overall compute, storage and network architecture, which increases the time taken to perform root cause analysis or can increase cost if not carefully planned and designed.
- Furthermore, as edge and fog computing pulls processing capabilities to a decentralised location, all physical assets must be secured, maintained and operated, meaning that the total cost of ownership can increase.
- Edge devices have a higher number of refresh cycles than a typical cloud infrastructure device, which results in ‘architecture design’ lock-in. This is an important aspect when designing the overall architecture as it restricts the amount of physical change the environment can cater for. It also means that the innovation cycles of new and existing IoT might be restricted, as the environment cannot accommodate these if fundamental changes to the edge hardware is needed.
Fog and edge computing are both able to filter data before it “hit” a big data lake for further consumption, reducing the amount of data that needs to be processed. The basic idea of edge and fog computing is to move data logic (mainly around data validation / data grammar checks) to an outer ring of capabilities.
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About the Author: Gunnar Menzel has been an IT professional for over 27 years and is VP and Chief Architect Officer for Capgemini’s Cloud Infrastructure Business. According to Richtopia Gunnar is one of the Top 50 most influential Information Technology Officer. His main focus is business – enabling technology transformation & innovation.