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Living on the edge: why edge computing is a positive trend for the channel


6 February 2018:

Edge computing is gradually becoming a prominent part of the Internet of Things architecture. Evan Unrue, Chief Technologist IoT & Analytics at Tech Data Advanced Solutions explains the concept and urges partners to jump on board or risk being left behind

Although the terms ‘Edge’ or ‘Fog’ computing have been around for some time, there is still a little confusion on what the terms mean and how they relate to the cloud and the Internet of Things (IoT). There are some subtle differences between Edge and Fog computing, but the goals are very much the same. Effectively, both terms refer to placing processing capability at the edge of the network and closer to the source of the data.

This is important for several reasons, key amongst these being the reduction of latency (how quickly data can be acted on), availability (ensuring data can still be acted on in the event that access to the cloud is lost) and efficiency (only sending data to the cloud where it’s needed).  


The IoT challenge

Gartner recently forecast that 8.4 billion connected things will be in use worldwide in 2017, up 31 per cent from 2016, and will reach 20.4 billion by 2020*. Gartner also stated that businesses are on pace to employ 3.1 billion connected things in 2017.

All these IoT devices are generating an unprecedented volume and variety of data. However, by the time the data is transferred to the cloud for analysis, the opportunity to act upon the findings might be lost.

Edge computing allows the analysis of critical and time sensitive data to occur close to where it is generated, thereby improving efficiency, instead of sending vast amounts of IoT data to the cloud. This enables businesses to have real-time responsiveness, based on pre-defined policies, at the network edge. In essence not all data captured in an IoT deployment is critical and needs to be sent to the cloud, which can be costly in terms of network traffic and financial cost if devices are deployed using technology such as GSM/LTE (3G/4G, etc).

However, the cloud, with its vast compute and storage resources, is best placed for performing big data analytics to derive broader business value. Therefore, data that is less time sensitive can be sent to the cloud for historical analysis, big data analytics and long-term storage.


Business use cases driving customer demand

IDC estimates that the amount of data analysed on devices that are physically close to the Internet of Things is approaching 40 per cent**; there is good reason why businesses are choosing to architect their environment in this way. Analysing IoT data close to where it is collected minimises latency, offloads gigabytes of network traffic from the core network, and keeps sensitive data within the network.

The Internet of Things is speeding up awareness and reaction to incidents. In industries such as oil and gas, manufacturing, utilities, transportation, mining, and the public sector, faster response time can improve output, enhance service levels, and increase safety.

In manufacturing, IoT sensors are used for equipment monitoring or predictive maintenance of critical machines, typically accessing data from existing machine controllers or programmable logic controllers (PLCs), coupled with additional sensors for monitoring vibration, acoustic or temperature. For example, a sensor on a drill machine could be used to detect unsafe levels of vibration associated with safety issues and imminent failure. Predefined policies would then enact safeguarding and lead to the immediate shut down of the machine and a service ticket being created to repair the machine to avoid costly equipment failure and a prolonged shutdown.

This example of simple analysis of vibration at the edge does not require access to the cloud to answer the simple question “is this machine vibrating too much”?  It may be that an answer would take too long or access to the cloud is not available at the relevant time.

However, it might be necessary to turn to the cloud to get a global dashboard of the health of all equipment across the estate. Metrics such as overall logged downtime can be enriched by MRP/ERP data to derive the cost impact of downtime in lost revenue. The data could also be used to do a deeper less time critical analysis of sensor data to learn how to optimise equipment settings on a particular make and model of machine.  This information could be based on observed data collected across hundreds of machines deployed across the customer’s estate, therefore having a massive impact in the reduction of downtime overall, not just for one machine.

There are significant business benefits to be achieved through the adoption of IoT.  However a rigid architecture that identifies traditional cloud computing as the only option limits potential. Real-time processing of data at the edge, coupled with advanced analytics and machine learning in the cloud provides organisations with the ideal architecture to meet their requirements and drive business insight.


The opportunity for the channel

Some technology trends can be speculative and others have transcended the hype curve. Edge computing is delivering real business value and is a prominent part of IOT architecture today and will become increasingly more so in the future.

Where the economics of accessing the cloud doesn’t stack up there is an argument for localised compute. Fog computing provides a companion to the cloud and a way to handle the vast amount of data generated daily from the Internet of Things. Organisations that adopt fog computing will gain deeper and faster insights, leading to increased business agility, higher service levels and improved safety.

Partners will benefit from taking a closure look at edge computing, particularly as it relies upon traditional on-premise skills that the channel has in abundance. It also helps the channel mitigate some of the erosion of hardware revenue caused by the adoption of cloud services. Now is the time for partners to try living on the edge and reap the positive benefits from fog/edge computing.



* Forecast: Internet of Things — Endpoints and Associated Services, Worldwide, 2016
** IDC FutureScape: Worldwide Internet of Things 2015 Predictions

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