The promise and challenges of edge computing - The EE

The promise and challenges of edge computing

Tom Urquhart of IOTech

What is edge computing?

Edge computing is a new computing model which promises to radically alter how businesses interact with the physical world, says Tom Urquhart, SVP, global edge solutions at IOTech.

Edge Computing refers to IoT device control and data processing power at the edge of the network, as close as possible to the operational sources of data. Data from each device, whether it be a machine or process sensor, robot, HVAC unit, autonomous car, or other intelligent device, is acquired, processed, analysed, and acted upon by dedicated edge computing platforms. The processed data can be acted upon locally and then selectively sent to the Cloud as required for further action and analysis.

Several factors are fueling the growth of edge computing. As the cost of operational technology (OT) sensors and processors decrease, the number of intelligent devices has been skyrocketing. It’s expected that in just two years’ time, 45% of all data created by industrial devices will be stored, processed, analysed, and acted upon close to or at the edge. In just three years, 5.6 billion of these devices will be connected to an edge Computing solution.

At the same time, the pace of business is intensifying, with instantaneous reaction times becoming a critical competitive advantage for many industries. Edge computing can help businesses very rapidly and inexpensively store, process, and analyse portions of their data closer to where it’s needed, reduce security risks and reaction times, and make it an important complement to Cloud Computing.

Benefits of edge computing

As edge computing goes mainstream, it can provide major strategic benefits for businesses:

Reduce Transmission Costs

With devices generating more data than ever, the cost of bandwidth for large-scale data transmission to the Cloud can soon add up. With edge computing, businesses can strategically choose which data they need to send to the Cloud, and which can be processed in a cost-effective fashion at the edge.

Ensure Security and Compliance

With edge computing, companies can more easily firewall and filter critical data and devices, processing locally on premise, and only expose selected data and applications to the Cloud.

Achieve ultra-low latency for real-time results

Regardless of network speed, data takes time to transfer over long distances. For systems that depend on real-time information, delays of even a few milliseconds can dictate success or failure. Edge computing greatly reduces latency, resulting in more responsive systems, and lowers the time it takes to glean actionable insights from OT data.

Operate Reliably even with Intermittent Connectivity

Edge computing enables manufacturing equipment and other smart devices to operate without disruption even when Internet connectivity is down or intermittent.

Ensure Interoperability between New and Legacy Devices

Edge computing software can convert the communication protocols used by legacy devices into a language that modern smart devices and Cloud applications can understand, making it easier to connect legacy industrial equipment with modern IoT platforms.

Challenges of edge computing

Edge computing, while undoubtedly offering great potential benefits to businesses, is not without its challenges:

Proliferation of devices, platforms and protocols

The OT world is characterised by heterogeneity – many different things, protocols, new vs. legacy hardware, etc. Ideally, Edge computing should act as a “shield” to this complexity, but there’s already a growing proliferation of often-incompatible edge computing platforms and applications on the market that could hinder wider adoption.

Open vs. Proprietary Systems

The need to adopt open edge standards and systems is and will become more critical. “Open,” at a minimum, means the edge computing Platforms must be silicon, hardware, operating system, software application and Cloud agnostic. We also need open standard APIs that can enable “plug and play” of any software application at the edge.

Time-Critical Performance

Many of the applications we want to run at the edge – including closed-loop control, specialist AI and analytics applications – need access to “real-time” data. These can be very challenging performance constraints, e.g., millisecond or even microsecond response times.

Scalability

Edge systems can be both very large-scale and highly distributed. This makes their smooth management both critical and difficult. While today’s enterprise management and deployment systems work very well in Enterprise / Cloud environments, they are not well-suited to edge deployments.

Hardware Constraints

The hardware available to run time-critical edge applications is often highly constrained in terms of memory availability or the need to run at low power. This means edge computing software may need to be highly optimised and have a very small “footprint.”

Open edge ecosystems

Getting the edge “right” is not just about technology, it is also about the global ecosystems that support it. A one-company “open” API is not really an open API, and the scale of the problem and the diversity at the edge requires collaboration: an ecosystem.

The good news is that there are open source ecosystems for the edge. LF edge is an umbrella organisation that has established an open, interoperable framework for edge computing.

LF edge’s leading project is edgeX foundry – a vendor-neutral open software platform hosted by the Linux Foundation that provides a common framework for industrial IoT edge computing. The edgeX Foundry project was established three years ago and has huge momentum; the project has just completed its sixth release (“Geneva”), with millions of downloads worldwide.

Closing Thoughts

The key challenges at the edge related to latency, network bandwidth, reliability, security, and OT heterogeneity cannot be addressed in Cloud-only models – the edge needs its own answers through edge computing platforms.

IOTech is in the business of providing those answers, with open software products and services at the IoT edge to help develop, deploy and manage edge computing, maximise user choice and flexibility, and enable effective collaboration across multiple vertical markets at the edge.

The author is Tom Urquhart, SVP, global edge solutions at IOTech.

About the author

Tom Urquhart, PhD is SVP, global edge solutions at IOTech and has over 35 years of IT and industry experience in sectors such as retail, FMCG, manufacturing, oil and gas, telecoms and utilities. He was previously Global Chief Architect at PwC. Tom has deep experience in middleware, data processing, ERP, integration and the application of these to help solve complex business problems. He holds a Ph.D. in Systems Engineering from the University of Cambridge.

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