The marketing team at Cisco introduced its fog computing vision in January 2014, also known as edge computing for other more purist vendors. However, it took until 2017 for fog to collect its most popular headlines thanks to the internet of things.
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
A study by IDC estimated that by 2020 10% of the world’s data will be produced by edge devices. This will further drive the need for more efficient fog computing technologies that provide low latency and holistic intelligence simultaneously.
“Computing at the edge of the network is, of course, not new — we’ve been doing it for years to solve the same issue with other kinds of computing.”
The problem with the cloud
As the internet of things proliferates, businesses face a growing need to analyze data from sources at the edge of a network, whether they are mobile phones, gateways or IoT sensors. Cloud computing has a disadvantage here: It can’t process data quickly enough for modern business applications.
IoT owes its explosive growth to the connection of physical things and operational technologies to analytics and machine learning applications, which can help glean insights from device-generated data and enable devices to make “smart” decisions without human intervention. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.
However, despite its power, the cloud model is not applicable to environments where operations are time-critical or internet connectivity is poor. This is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle-to-vehicle communications, where the prevention of collisions and accidents can’t afford the latency caused by the roundtrip to the cloud server.
“The cloud paradigm is like having your brain command your limbs from miles away — it won’t help you where you need quick reflexes.”
Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries.
IoT nodes are closer to the action, but for the moment they do not have the computing and storage resources to perform analytics and machine learning tasks. Cloud servers, on the other hand, have the horsepower, but are too far away to process data and respond in time.
The fog/edge layer is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge and support the local ingestion of data and the quick turnaround of results.
The OpenFog Consortium and EdgeX Foundry
The OpenFog Consortium was founded on the premise based on open architectures and standards that are essential for the success of a ubiquitous fog computing ecosystem.
The collaboration among tech giants such as ARM, Cisco, Dell, GE, Intel, Microsoft and Schneider Electric defining an open, interoperable fog computing architecture was without any doubt good news for a vibrant supplier ecosystem.
EdgeX Foundry is a vendor-neutral open source project hosted by the Linux Foundation building a common open framework for IoT edge computing. At the heart of the project is an interoperability framework hosted within a full hardware- and OS-agnostic reference software platform to enable an ecosystem of plug-and-play components that unifies the marketplace and accelerates the deployment of IoT solutions.
Benefits of fog/edge computing
- Frees up network capacity — Fog computing uses much less bandwidth, which means it doesn’t cause bottlenecks and other similar occupancies. Less data movement on the network frees up network capacity, which then can be used for other things.
- It is truly real time — Fog computing has much higher expedience than any other cloud computing architecture we know today. Since all data analysis is being done at the spot, it represents a true real-time concept, which means it is a perfect match for the needs of internet of things concepts.
- Boosts data security — Collected data is more secure when it doesn’t travel. It also makes data storing much simpler because it stays in its country of origin. Sending data abroad might violate certain laws.
Disadvantages of fog/edge computing
- Analytics is done locally — The fog computing concept enables developers to access the most important IoT data from other locations, but it still keeps piles of less important information in local storages.
- Some companies don’t like their data being out of their premises — With fog computing, lots of data is stored on the devices themselves (which are often located outside of company offices), this is perceived as a risk by parts of some developers’ communities.
- Whole system sounds a little bit confusing — Concept that includes huge numbers of devices that store, analyze and send their own data and are located all around the world sounds utterly confusing.
What is the future of fog/edge computing?
The current trend shows that fog computing will continue to grow in usage and importance as the internet of things expands and conquers new grounds. With inexpensive, low-power processing and storage becoming more available, we can expect computation to move even closer to the edge and become ingrained in the same devices that are generating the data, creating even greater possibilities for inter-device intelligence and interactions. Sensors that only log data might one day become a thing of the past.
Fog/edge computing will be the next big thing in the internet of things — at least in the next couple of years. It seems obvious that while cloud is a perfect match for the internet of things, we have other scenarios and IoT technologies that demand low-latency ingestion and immediate processing of data where fog computing is the answer.
Does the fog/edge computing eliminate the cloud?
Fog/edge computing improves efficiency and reduces the amount of data that needs to be sent to the cloud for processing. But it’s here to complement the cloud, not replace it.
The cloud will continue to have a pertinent role in the IoT cycle. In fact, with fog computing shouldering the burden of short-term analytics at the edge, cloud resources will be freed to take on the heavier tasks, especially where the analysis of historical data and large datasets is concerned. Insights obtained by the cloud can help update and tweak policies and functionality at the fog layer.
“It is the combination of fog and cloud computing that will accelerate the adoption of IoT, especially for the enterprise.”
Thanks in advance for your likes and shares!
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.