Over the past few years, most organisations have begun to incorporate cloud into their operations, gathering insights from large amounts of data that help them achieve key business outcomes.
In fact, cloud traffic is almost four times by 2020. Simultaneously, the Internet of Things (IoT) market is in the same period.
This combination results in unprecedented volumes of data, which can easily become unmanageable. While cloud computing is a major enabler particularly for industrial transformation, there is an to turn the massive amounts of machine-based data into actionable intelligence closer to the source.
That’s where edge computing comes in.
As more computing, storage and analytical capacity is bundled into smaller devices that sit closer to the data, edge computing pushing computer applications, data and services away from the centralised cloud to the logical extremes of a network (e.g. mobile devices, autonomous vehicles or industrial machines). This means data is turned into insightful and intelligent actions at the source of the data, which can reduce unplanned downtime, improve asset performance, lower the cost of maintenance and increase production efficiency.
So, where does that leave cloud computing?
The shift to centralised cloud computing is causing the wave of edge computing to . For the two to work most effectively, they need to work in tandem.
Cloud computing remains important when it comes to actions requiring significant computing power, managing data volumes from different parts of the organisation, asset monitoring and machine learning. In other words, you would still use the cloud for processing that’s not time-sensitive or is not needed for one device to take immediate action, but rather helps the organisation to make business-wide decisions.
On the other hand, edge computing comes into play when there is a need for immediate actions, there are connectivity constraints or there is a need for flexibility and customisation of processes and products. That is, the goal is to process the data from the device that needs it quickly, so that it can take the next most relevant action. There are many cases where reaction time is the key value of IoT, and consistently sending the data back to the centralised cloud prevents that value from being realised.
Let’s look at this in real terms.
In a fleet management scenario, data is gathered from multiple operational points, such as the wheels, brakes, battery and electrical system. Using edge computing, the fleet manager can monitor each of these points on each of the vehicles in the fleet and proactively service the vehicle, maximising uptime and reducing costs.
At the same time, data from all the vehicles is sent to the cloud where it is aggregated and analysed to monitor the health of these key components over time. The fleet manager can use these insights to track the average cost of a given truck model over time, which helps the organisation make informed decisions that impact the overall costs of its fleet.
We can similarly see how cloud and edge computing work together when it comes to . Intel estimates that autonomous cars, with hundreds of on-vehicle sensors, will for an hour and a half of driving. Most of this data does not need to be sent to the cloud – in fact, it would be unsafe, unnecessary and impractical to do so.
This is an ideal example of how edge computing can assist in cases where real-time decision-making is key. In the case of a child running out in front of the car while it’s driving, it would be of little use to have it sending data to the cloud for analysis and decision-making. Rather, the data needs to be analysed at the edge so that immediate action (applying the brakes) can be taken.
However, the cloud still has a role to play here too. The data showing that the car had to respond to such an immediate event may be valuable to the car manufacturer as it works on building future models.
There’s no doubt that the proliferation of data is set to continue.
The Middle East and Africa is expected to see an , estimated to reach 451 exabytes (one quintillion bytes) per year by 2020. This is one reason for Microsoft’s recently announced . Cloud computing supports the digital transformation of businesses, while analytics spurs innovation.
Edge computing adds another important layer to this. It moves the insights currently sitting in the cloud down to the device where all the action, ensuring faster action and better customer satisfaction in the long run.
Combining cloud and edge computing ensures that businesses have both the big data and the more granular data they need to succeed in the short and long term.
The write is Country GM - Microsoft East & Southern Africa